---------------------------------------------------------------------------------------
. xtset firm_id year
       panel variable:  firm_id (unbalanced)
        time variable:  year, 2010 to 2019
                delta:  1 unit

. 
. tsset  firm_id  year
       panel variable:  firm_id (unbalanced)
        time variable:  year, 2010 to 2019
                delta:  1 unit

. 
. xtdes, i(firm_id) t(year)

 firm_id:  243, 245, ..., 8011                               n =        147
    year:  2010, 2011, ..., 2019                             T =         10
           Delta(year) = 1 unit
           Span(year)  = 10 periods
           (firm_id*year uniquely identifies each observation)

Distribution of T_i:   min      5%     25%       50%       75%     95%     max
                         6       7      10        10        10      10      10

     Freq.  Percent    Cum. |  Pattern
 ---------------------------+------------
      120     81.63   81.63 |  1111111111
        7      4.76   86.39 |  11111111..
        6      4.08   90.48 |  111111....
        6      4.08   94.56 |  111111111.
        3      2.04   96.60 |  .111111111
        3      2.04   98.64 |  1111111...
        1      0.68   99.32 |  ....111111
        1      0.68  100.00 |  ..11111111
 ---------------------------+------------
      147    100.00         |  XXXXXXXXXX


. **CG INDEX
. **TABELA 6 - PAINEL A - TESTES
. regress divsubattotal cgindex votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 1!=.

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.73
       Model |  .749029488        29  .025828603   Prob > F        =    0.0000
    Residual |  7.40825395     1,357  .005459288   R-squared       =    0.0918
-------------+----------------------------------   Adj R-squared   =    0.0724
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07389

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |   -.022257   .0236037    -0.94   0.346    -.0685607    .0240467
     votsom1 |  -.0580755   .0339082    -1.71   0.087    -.1245938    .0084427
     vots1_2 |   .0766739   .0339267     2.26   0.024     .0101195    .1432283
      roe_ll |  -.0046153   .0134013    -0.34   0.731    -.0309048    .0216742
      tangib |   .0324304   .0130905     2.48   0.013     .0067506    .0581102
   lnattotal |   .0029868   .0016676     1.79   0.074    -.0002846    .0062581
             |
     catset1 |
          2  |   .0265647   .0099812     2.66   0.008     .0069845    .0461449
          3  |  -.0172826   .0143594    -1.20   0.229    -.0454516    .0108864
          4  |   .0107991   .0124804     0.87   0.387    -.0136839    .0352821
          5  |   .0025598    .011056     0.23   0.817     -.019129    .0242486
          6  |  -.0123495   .0121228    -1.02   0.309    -.0361309    .0114319
          7  |  -.0167724   .0102591    -1.63   0.102    -.0368979     .003353
          8  |   -.016905   .0098539    -1.72   0.086    -.0362355    .0024254
          9  |  -.0119048   .0163197    -0.73   0.466    -.0439193    .0201098
         10  |  -.0104717   .0104432    -1.00   0.316    -.0309583    .0100149
         11  |    -.02446   .0112042    -2.18   0.029    -.0464394   -.0024805
         12  |   .0236369   .0101225     2.34   0.020     .0037795    .0434943
         13  |   .0453939   .0155499     2.92   0.004     .0148895    .0758984
         98  |  -.0167271   .0096112    -1.74   0.082    -.0355816    .0021273
         99  |   .0007891   .0138021     0.06   0.954    -.0262868    .0278649
             |
        year |
       2011  |  -.0018242   .0090198    -0.20   0.840    -.0195184    .0158701
       2012  |   .0061531   .0090372     0.68   0.496    -.0115753    .0238815
       2013  |   .0077052   .0090198     0.85   0.393    -.0099891    .0253995
       2014  |   .0131688   .0090771     1.45   0.147     -.004638    .0309755
       2015  |   .0181483   .0091846     1.98   0.048     .0001308    .0361659
       2016  |  -.0094592   .0092688    -1.02   0.308     -.027642    .0087236
       2017  |  -.0084231   .0093077    -0.90   0.366    -.0266822     .009836
       2018  |  -.0017126   .0095084    -0.18   0.857    -.0203653    .0169402
       2019  |  -.0069535   .0095541    -0.73   0.467    -.0256959    .0117889
             |
       _cons |  -.0050457   .0296386    -0.17   0.865    -.0631882    .0530968
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.46    0.686223
     votsom1 |     15.58    0.064184
     vots1_2 |     15.09    0.066274
      roe_ll |      1.15    0.872884
      tangib |      1.22    0.820662
   lnattotal |      1.50    0.666948
     catset1 |
          2  |      2.09    0.478370
          3  |      1.36    0.735200
          4  |      1.79    0.557580
          5  |      1.81    0.553653
          6  |      1.77    0.566556
          7  |      2.26    0.443324
          8  |      2.48    0.403825
          9  |      1.29    0.774260
         10  |      2.02    0.494265
         11  |      1.85    0.539104
         12  |      2.09    0.478838
         13  |      1.30    0.769229
         98  |      2.86    0.349658
         99  |      1.52    0.658196
        year |
       2011  |      1.88    0.533120
       2012  |      1.91    0.524430
       2013  |      1.95    0.513681
       2014  |      1.98    0.504170
       2015  |      2.03    0.492439
       2016  |      1.99    0.501680
       2017  |      1.97    0.507087
       2018  |      1.96    0.509022
       2019  |      1.90    0.525852
-------------+----------------------
    Mean VIF |      2.76

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots1_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.033     1    0.0000 |   1 |    1033.033     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   874.18
         Prob > chi2  =   0.0000

. **xtgls divsubattotal cgindex votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 1!=., i(firm_id) t(year)
. **utest votsom1 vots1_2
. 
. regress divsubattotal cgindex votsom2 vots2_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 2!=. 

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.74
       Model |  .750815046        29  .025890174   Prob > F        =    0.0000
    Residual |  7.40646839     1,357  .005457972   R-squared       =    0.0920
-------------+----------------------------------   Adj R-squared   =    0.0726
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07388

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.0233774   .0239241    -0.98   0.329    -.0703097    .0235549
     votsom2 |  -.0938549   .0393052    -2.39   0.017    -.1709605   -.0167493
     vots2_2 |   .0930834    .034744     2.68   0.007     .0249256    .1612412
      roe_ll |  -.0038532   .0133849    -0.29   0.773    -.0301105    .0224042
      tangib |   .0315564   .0131177     2.41   0.016     .0058232    .0572896
   lnattotal |   .0026319   .0016671     1.58   0.115    -.0006385    .0059023
             |
     catset1 |
          2  |   .0256799   .0100276     2.56   0.011     .0060087    .0453511
          3  |  -.0180159   .0143795    -1.25   0.210    -.0462243    .0101924
          4  |   .0092381   .0124966     0.74   0.460    -.0152767    .0337529
          5  |   .0006788   .0111052     0.06   0.951    -.0211064    .0224639
          6  |   -.014474   .0120928    -1.20   0.232    -.0381967    .0092487
          7  |  -.0184731   .0103172    -1.79   0.074    -.0387126    .0017664
          8  |  -.0191027   .0100989    -1.89   0.059    -.0389138    .0007085
          9  |  -.0127679   .0163485    -0.78   0.435    -.0448391    .0193032
         10  |  -.0118206   .0104789    -1.13   0.260    -.0323771     .008736
         11  |  -.0235139   .0111415    -2.11   0.035    -.0453704   -.0016574
         12  |   .0219971   .0101808     2.16   0.031     .0020253    .0419689
         13  |   .0429578   .0155504     2.76   0.006     .0124523    .0734633
         98  |  -.0184396   .0096713    -1.91   0.057    -.0374119    .0005327
         99  |   .0005136   .0138034     0.04   0.970    -.0265646    .0275919
             |
        year |
       2011  |  -.0015717     .00902    -0.17   0.862    -.0192663     .016123
       2012  |   .0059954   .0090363     0.66   0.507    -.0117311     .023722
       2013  |   .0077507   .0090199     0.86   0.390    -.0099437    .0254451
       2014  |   .0133424   .0090795     1.47   0.142    -.0044689    .0311537
       2015  |   .0184262   .0091891     2.01   0.045     .0003998    .0364525
       2016  |  -.0089313   .0092724    -0.96   0.336    -.0271212    .0092586
       2017  |  -.0079539   .0093072    -0.85   0.393     -.026212    .0103041
       2018  |  -.0013673   .0095108    -0.14   0.886    -.0200247    .0172901
       2019  |  -.0069851   .0095528    -0.73   0.465     -.025725    .0117548
             |
       _cons |   .0136101   .0311433     0.44   0.662    -.0474841    .0747043
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.50    0.667803
     votsom2 |     21.01    0.047597
     vots2_2 |     21.07    0.047467
      roe_ll |      1.14    0.874813
      tangib |      1.22    0.817062
   lnattotal |      1.50    0.667169
     catset1 |
          2  |      2.11    0.473839
          3  |      1.36    0.732973
          4  |      1.80    0.556001
          5  |      1.82    0.548632
          6  |      1.76    0.569230
          7  |      2.28    0.438238
          8  |      2.60    0.384374
          9  |      1.30    0.771344
         10  |      2.04    0.490790
         11  |      1.83    0.545056
         12  |      2.11    0.473254
         13  |      1.30    0.768992
         98  |      2.90    0.345244
         99  |      1.52    0.657920
        year |
       2011  |      1.88    0.532966
       2012  |      1.91    0.524413
       2013  |      1.95    0.513550
       2014  |      1.98    0.503790
       2015  |      2.03    0.491841
       2016  |      2.00    0.501166
       2017  |      1.97    0.507024
       2018  |      1.97    0.508646
       2019  |      1.90    0.525868
-------------+----------------------
    Mean VIF |      3.16

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1073.819     1    0.0000 |   1 |    1073.819     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots2_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1073.196     1    0.0000 |   1 |    1073.196     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   849.44
         Prob > chi2  =   0.0000

. **xtgls divsubattotal cgindex votsom2 vots2_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 2!=., i(firm_id) t(year)
. **utest votsom2 vots2_2
. 
. regress divsubattotal cgindex votsom3 vots3_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 3!=. 

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.74
       Model |  .750194945        29  .025868791   Prob > F        =    0.0000
    Residual |  7.40708849     1,357  .005458429   R-squared       =    0.0920
-------------+----------------------------------   Adj R-squared   =    0.0726
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07388

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.0220847   .0241656    -0.91   0.361    -.0694907    .0253214
     votsom3 |  -.1069184    .043881    -2.44   0.015    -.1930004   -.0208364
     vots3_2 |   .0981724   .0366099     2.68   0.007     .0263543    .1699904
      roe_ll |  -.0037629     .01338    -0.28   0.779    -.0300107    .0224849
      tangib |   .0327371   .0130605     2.51   0.012     .0071162     .058358
   lnattotal |   .0024855   .0016699     1.49   0.137    -.0007904    .0057614
             |
     catset1 |
          2  |   .0257377   .0100245     2.57   0.010     .0060725    .0454029
          3  |  -.0176018   .0143752    -1.22   0.221    -.0458017    .0105982
          4  |   .0093274   .0125028     0.75   0.456    -.0151995    .0338543
          5  |   .0001139   .0111317     0.01   0.992    -.0217232    .0219511
          6  |   -.014862   .0120801    -1.23   0.219    -.0385597    .0088357
          7  |  -.0186257   .0103299    -1.80   0.072      -.03889    .0016386
          8  |  -.0184874   .0100545    -1.84   0.066    -.0382114    .0012366
          9  |  -.0136801     .01637    -0.84   0.403    -.0457934    .0184331
         10  |  -.0124881   .0105027    -1.19   0.235    -.0330914    .0081151
         11  |  -.0228773   .0111161    -2.06   0.040     -.044684   -.0010706
         12  |   .0216604   .0102035     2.12   0.034     .0016441    .0416768
         13  |   .0437329   .0155143     2.82   0.005     .0132983    .0741676
         98  |  -.0185564   .0096759    -1.92   0.055    -.0375377     .000425
         99  |   .0004327   .0138043     0.03   0.975    -.0266473    .0275128
             |
        year |
       2011  |  -.0014592   .0090229    -0.16   0.872    -.0191595    .0162411
       2012  |   .0059993    .009038     0.66   0.507    -.0117307    .0237293
       2013  |   .0078897   .0090245     0.87   0.382    -.0098138    .0255932
       2014  |   .0134088   .0090854     1.48   0.140    -.0044142    .0312317
       2015  |   .0187041   .0091944     2.03   0.042     .0006673    .0367408
       2016  |  -.0088186    .009278    -0.95   0.342    -.0270193    .0093821
       2017  |  -.0078699   .0093076    -0.85   0.398    -.0261288     .010389
       2018  |  -.0013387   .0095123    -0.14   0.888    -.0199991    .0173217
       2019  |   -.007154   .0095533    -0.75   0.454    -.0258948    .0115869
             |
       _cons |    .020215   .0325887     0.62   0.535    -.0437147    .0841448
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.53    0.654579
     votsom3 |     23.89    0.041860
     vots3_2 |     23.98    0.041694
      roe_ll |      1.14    0.875524
      tangib |      1.21    0.824310
   lnattotal |      1.50    0.664976
     catset1 |
          2  |      2.11    0.474168
          3  |      1.36    0.733471
          4  |      1.80    0.555497
          5  |      1.83    0.546069
          6  |      1.75    0.570478
          7  |      2.29    0.437201
          8  |      2.58    0.387812
          9  |      1.30    0.769386
         10  |      2.05    0.488606
         11  |      1.83    0.547596
         12  |      2.12    0.471189
         13  |      1.29    0.772641
         98  |      2.90    0.344944
         99  |      1.52    0.657885
        year |
       2011  |      1.88    0.532672
       2012  |      1.91    0.524250
       2013  |      1.95    0.513064
       2014  |      1.99    0.503173
       2015  |      2.04    0.491316
       2016  |      2.00    0.500613
       2017  |      1.97    0.507017
       2018  |      1.97    0.508524
       2019  |      1.90    0.525858
-------------+----------------------
    Mean VIF |      3.37

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom3

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1053.856     1    0.0000 |   1 |    1053.856     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots3_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1060.826     1    0.0000 |   1 |    1060.826     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   849.29
         Prob > chi2  =   0.0000

. ****xtgls divsubattotal cgindex votsom3 vots3_2 roe_ll tangib lnattotal i.catset1 i.y
> ear if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & vots
> om3!=., i(firm_id) t(year)
. **utest votsom3 vots3_2
. 
. regress divsubattotal cgindex votsom4 vots4_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 4!=. 

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.69
       Model |  .742646131        29  .025608487   Prob > F        =    0.0000
    Residual |  7.41463731     1,357  .005463992   R-squared       =    0.0910
-------------+----------------------------------   Adj R-squared   =    0.0716
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07392

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.0239909   .0242924    -0.99   0.324    -.0716456    .0236637
     votsom4 |  -.1079559   .0473964    -2.28   0.023    -.2009341   -.0149776
     vots4_2 |   .0941983   .0383955     2.45   0.014     .0188772    .1695193
      roe_ll |  -.0036831    .013387    -0.28   0.783    -.0299446    .0225784
      tangib |   .0338374   .0130366     2.60   0.010     .0082633    .0594115
   lnattotal |   .0024769   .0016722     1.48   0.139    -.0008034    .0057572
             |
     catset1 |
          2  |   .0261902   .0100279     2.61   0.009     .0065183     .045862
          3  |  -.0179905   .0143908    -1.25   0.211    -.0462211    .0102402
          4  |    .009719   .0125005     0.78   0.437    -.0148033    .0342414
          5  |   .0003423   .0111431     0.03   0.975    -.0215173     .022202
          6  |  -.0155085   .0121079    -1.28   0.200    -.0392606    .0082437
          7  |  -.0186368   .0103305    -1.80   0.071    -.0389023    .0016288
          8  |  -.0176344    .010072    -1.75   0.080    -.0373929     .002124
          9  |  -.0128479   .0163688    -0.78   0.433    -.0449588     .019263
         10  |  -.0127574   .0105382    -1.21   0.226    -.0334304    .0079157
         11  |  -.0226498   .0111362    -2.03   0.042    -.0444958   -.0008037
         12  |   .0221615   .0101944     2.17   0.030     .0021631    .0421599
         13  |   .0441001   .0155168     2.84   0.005     .0136605    .0745397
         98  |  -.0182956   .0096682    -1.89   0.059    -.0372618    .0006706
         99  |   .0000126   .0138058     0.00   0.999    -.0270703    .0270956
             |
        year |
       2011  |  -.0014983   .0090293    -0.17   0.868    -.0192111    .0162146
       2012  |    .005914   .0090433     0.65   0.513    -.0118263    .0236543
       2013  |   .0080115   .0090337     0.89   0.375      -.00971    .0257329
       2014  |   .0134081    .009096     1.47   0.141    -.0044356    .0312517
       2015  |     .01887   .0092058     2.05   0.041     .0008109     .036929
       2016  |  -.0088537   .0092884    -0.95   0.341    -.0270749    .0093675
       2017  |  -.0078131   .0093142    -0.84   0.402    -.0260849    .0104587
       2018  |  -.0012819   .0095204    -0.13   0.893    -.0199582    .0173944
       2019  |  -.0071939   .0095596    -0.75   0.452    -.0259471    .0115594
             |
       _cons |   .0234655   .0336946     0.70   0.486    -.0426336    .0895647
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.54    0.648426
     votsom4 |     25.66    0.038970
     vots4_2 |     25.79    0.038768
      roe_ll |      1.14    0.875499
      tangib |      1.21    0.828172
   lnattotal |      1.51    0.663871
     catset1 |
          2  |      2.11    0.474332
          3  |      1.36    0.732623
          4  |      1.80    0.556272
          5  |      1.83    0.545501
          6  |      1.76    0.568443
          7  |      2.29    0.437592
          8  |      2.58    0.386854
          9  |      1.30    0.770283
         10  |      2.06    0.485810
         11  |      1.83    0.546181
         12  |      2.12    0.472515
         13  |      1.29    0.773177
         98  |      2.89    0.345847
         99  |      1.52    0.658417
        year |
       2011  |      1.88    0.532460
       2012  |      1.91    0.524177
       2013  |      1.95    0.512548
       2014  |      1.99    0.502516
       2015  |      2.04    0.490602
       2016  |      2.00    0.499997
       2017  |      1.97    0.506819
       2018  |      1.97    0.508177
       2019  |      1.90    0.525696
-------------+----------------------
    Mean VIF |      3.49

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom4

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.489     1    0.0000 |   1 |    1033.489     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots4_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1047.969     1    0.0000 |   1 |    1047.969     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   839.45
         Prob > chi2  =   0.0000

. **xtgls divsubattotal cgindex votsom4 vots4_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 4!=., i(firm_id) t(year)
. **utest votsom4 vots4_2
. 
. regress divsubattotal cgindex votsom5 vots5_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 5!=. 

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.62
       Model |  .733634537        29  .025297743   Prob > F        =    0.0000
    Residual |   7.4236489     1,357  .005470633   R-squared       =    0.0899
-------------+----------------------------------   Adj R-squared   =    0.0705
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07396

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.0244298   .0243666    -1.00   0.316    -.0722301    .0233705
     votsom5 |  -.0945382   .0492828    -1.92   0.055     -.191217    .0021406
     vots5_2 |   .0821291   .0393886     2.09   0.037     .0048599    .1593983
      roe_ll |  -.0037856   .0133965    -0.28   0.778    -.0300657    .0224946
      tangib |   .0347588   .0130259     2.67   0.008     .0092057    .0603119
   lnattotal |   .0025524   .0016726     1.53   0.127    -.0007288    .0058335
             |
     catset1 |
          2  |   .0265447   .0100316     2.65   0.008     .0068656    .0462238
          3  |  -.0177569   .0143973    -1.23   0.218    -.0460003    .0104866
          4  |     .01081    .012478     0.87   0.386    -.0136684    .0352883
          5  |   .0011415   .0111336     0.10   0.918    -.0206994    .0229824
          6  |  -.0151468   .0121187    -1.25   0.212    -.0389203    .0086267
          7  |  -.0181284   .0103238    -1.76   0.079    -.0383808    .0021241
          8  |  -.0168964   .0100714    -1.68   0.094    -.0366535    .0028608
          9  |  -.0124502    .016374    -0.76   0.447    -.0445714     .019671
         10  |  -.0122743   .0105404    -1.16   0.244    -.0329516    .0084029
         11  |  -.0223114   .0111516    -2.00   0.046    -.0441877   -.0004351
         12  |   .0229095   .0101841     2.25   0.025     .0029311    .0428878
         13  |   .0444822   .0155224     2.87   0.004     .0140317    .0749328
         98  |  -.0175911   .0096542    -1.82   0.069    -.0365299    .0013477
         99  |  -.0002736   .0138121    -0.02   0.984     -.027369    .0268218
             |
        year |
       2011  |  -.0016241   .0090348    -0.18   0.857    -.0193477    .0160995
       2012  |    .005805   .0090487     0.64   0.521     -.011946    .0235559
       2013  |   .0078883   .0090414     0.87   0.383    -.0098484    .0256251
       2014  |   .0133477   .0091064     1.47   0.143    -.0045164    .0312118
       2015  |   .0187685   .0092184     2.04   0.042     .0006847    .0368523
       2016  |  -.0089341   .0092992    -0.96   0.337    -.0271764    .0093083
       2017  |  -.0078357   .0093214    -0.84   0.401    -.0261216    .0104502
       2018  |  -.0012959   .0095284    -0.14   0.892    -.0199878     .017396
       2019  |  -.0070529    .009565    -0.74   0.461    -.0258166    .0117108
             |
       _cons |   .0189962   .0342767     0.55   0.580    -.0482448    .0862373
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.55    0.645264
     votsom5 |     26.11    0.038303
     vots5_2 |     26.26    0.038079
      roe_ll |      1.14    0.875319
      tangib |      1.20    0.830543
   lnattotal |      1.51    0.664335
     catset1 |
          2  |      2.11    0.474558
          3  |      1.36    0.732849
          4  |      1.79    0.558952
          5  |      1.83    0.547102
          6  |      1.76    0.568114
          7  |      2.28    0.438693
          8  |      2.58    0.387376
          9  |      1.30    0.770725
         10  |      2.06    0.486201
         11  |      1.83    0.545334
         12  |      2.11    0.474040
         13  |      1.29    0.773559
         98  |      2.88    0.347270
         99  |      1.52    0.658611
        year |
       2011  |      1.88    0.532459
       2012  |      1.91    0.524184
       2013  |      1.95    0.512288
       2014  |      1.99    0.501977
       2015  |      2.04    0.489854
       2016  |      2.00    0.499444
       2017  |      1.97    0.506652
       2018  |      1.97    0.507946
       2019  |      1.90    0.525748
-------------+----------------------
    Mean VIF |      3.52

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom5

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1015.172     1    0.0000 |   1 |    1015.172     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots5_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1036.555     1    0.0000 |   1 |    1036.555     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   828.57
         Prob > chi2  =   0.0000

. **xtgls divsubattotal cgindex votsom5 vots5_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 5!=., i(firm_id) t(year)
. **utest votsom5 vots5_2
. 
. ** EXVOT 1
. regress divsubattotal cgindex exvot1 roe_ll tangib lnattotal i.catset1 i.year if divs
> ubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=.

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(28, 1358)     =      4.82
       Model |  .738067284        28  .026359546   Prob > F        =    0.0000
    Residual |  7.41921615     1,358   .00546334   R-squared       =    0.0905
-------------+----------------------------------   Adj R-squared   =    0.0717
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07391

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.0167375   .0227928    -0.73   0.463    -.0614504    .0279753
      exvot1 |   .0397225   .0171841     2.31   0.021     .0060124    .0734327
      roe_ll |  -.0045744   .0133881    -0.34   0.733    -.0308381    .0216893
      tangib |   .0317276   .0131795     2.41   0.016     .0058732     .057582
   lnattotal |   .0022762   .0016826     1.35   0.176    -.0010246    .0055771
             |
     catset1 |
          2  |   .0241229   .0101545     2.38   0.018     .0042028    .0440431
          3  |  -.0190187   .0142924    -1.33   0.184    -.0470563    .0090188
          4  |   .0114428   .0123901     0.92   0.356     -.012863    .0357486
          5  |   .0002076   .0111658     0.02   0.985    -.0216965    .0221117
          6  |  -.0137527   .0120586    -1.14   0.254    -.0374081    .0099027
          7  |   -.017608   .0102478    -1.72   0.086    -.0377112    .0024953
          8  |   -.019573   .0102233    -1.91   0.056    -.0396281    .0004822
          9  |  -.0154995   .0164477    -0.94   0.346    -.0477652    .0167662
         10  |  -.0107169   .0104507    -1.03   0.305    -.0312183    .0097844
         11  |  -.0257402   .0113823    -2.26   0.024    -.0480691   -.0034114
         12  |    .021229    .010273     2.07   0.039     .0010764    .0413817
         13  |   .0435569   .0155342     2.80   0.005     .0130833    .0740304
         98  |   -.016889   .0095661    -1.77   0.078    -.0356549    .0018769
         99  |  -.0021069   .0138321    -0.15   0.879    -.0292415    .0250277
             |
        year |
       2011  |  -.0019992   .0090206    -0.22   0.825    -.0196951    .0156967
       2012  |   .0056476   .0090382     0.62   0.532    -.0120827    .0233779
       2013  |   .0072804   .0090206     0.81   0.420    -.0104154    .0249761
       2014  |   .0130862   .0090744     1.44   0.150    -.0047153    .0308877
       2015  |     .01804   .0091744     1.97   0.049     .0000426    .0360375
       2016  |  -.0092996   .0092558    -1.00   0.315    -.0274568    .0088576
       2017  |  -.0083375   .0092986    -0.90   0.370    -.0265786    .0099037
       2018  |   -.002092   .0094993    -0.22   0.826    -.0207268    .0165428
       2019  |  -.0070156   .0095564    -0.73   0.463    -.0257625    .0117313
             |
       _cons |  -.0061704   .0275272    -0.22   0.823    -.0601708      .04783
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.36    0.736468
      exvot1 |      1.52    0.658049
      roe_ll |      1.14    0.875250
      tangib |      1.23    0.810214
   lnattotal |      1.53    0.655565
     catset1 |
          2  |      2.16    0.462523
          3  |      1.35    0.742657
          4  |      1.77    0.566159
          5  |      1.84    0.543223
          6  |      1.75    0.573033
          7  |      2.25    0.444635
          8  |      2.66    0.375447
          9  |      1.31    0.762816
         10  |      2.02    0.493920
         11  |      1.91    0.522753
         12  |      2.15    0.465256
         13  |      1.30    0.771361
         98  |      2.83    0.353227
         99  |      1.52    0.655834
        year |
       2011  |      1.87    0.533417
       2012  |      1.91    0.524704
       2013  |      1.95    0.513977
       2014  |      1.98    0.504842
       2015  |      2.02    0.493906
       2016  |      1.99    0.503469
       2017  |      1.97    0.508464
       2018  |      1.96    0.510380
       2019  |      1.90    0.525989
-------------+----------------------
    Mean VIF |      1.83

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   833.03
         Prob > chi2  =   0.0000

. **xtgls   divsubattotal cgindex exvot1 roe_ll tangib lnattotal i.catset1 i.year if di
> vsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=., i
> (firm_id) t(year)
. 
. regress divsubattotal cgindex exvot1 votsom1 roe_ll tangib lnattotal i.catset1 i.year
>  if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1
> !=.

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.66
       Model |  .739340196        29   .02549449   Prob > F        =    0.0000
    Residual |  7.41794324     1,357  .005466428   R-squared       =    0.0906
-------------+----------------------------------   Adj R-squared   =    0.0712
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07394

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.0136104   .0237023    -0.57   0.566    -.0601075    .0328866
      exvot1 |   .0353861   .0193962     1.82   0.068    -.0026637    .0734359
     votsom1 |   .0054873   .0113714     0.48   0.629    -.0168201    .0277947
      roe_ll |  -.0049009    .013409    -0.37   0.715    -.0312055    .0214037
      tangib |   .0320455   .0131997     2.43   0.015     .0061515    .0579395
   lnattotal |   .0023328   .0016872     1.38   0.167     -.000977    .0056425
             |
     catset1 |
          2  |   .0241982   .0101585     2.38   0.017       .00427    .0441263
          3  |  -.0181262   .0144156    -1.26   0.209    -.0464055     .010153
          4  |   .0120347   .0124542     0.97   0.334    -.0123968    .0364662
          5  |   .0005467    .011191     0.05   0.961     -.021407    .0225003
          6  |  -.0129644   .0121721    -1.07   0.287    -.0368425    .0109138
          7  |  -.0171498   .0102946    -1.67   0.096    -.0373448    .0030452
          8  |  -.0194888   .0102277    -1.91   0.057    -.0395526    .0005749
          9  |  -.0155714   .0164531    -0.95   0.344    -.0478476    .0167048
         10  |  -.0107889   .0104548    -1.03   0.302    -.0312982    .0097203
         11  |  -.0254104   .0114061    -2.23   0.026    -.0477858    -.003035
         12  |    .021729    .010328     2.10   0.036     .0014685    .0419895
         13  |   .0430998   .0155674     2.77   0.006     .0125611    .0736386
         98  |  -.0163785   .0096271    -1.70   0.089    -.0352641    .0025071
         99  |  -.0016575   .0138673    -0.12   0.905    -.0288612    .0255462
             |
        year |
       2011  |  -.0020762   .0090246    -0.23   0.818    -.0197798    .0156275
       2012  |    .005619   .0090409     0.62   0.534    -.0121168    .0233547
       2013  |    .007196   .0090248     0.80   0.425     -.010508    .0249001
       2014  |   .0129286   .0090829     1.42   0.155    -.0048894    .0307466
       2015  |   .0178007   .0091903     1.94   0.053    -.0002281    .0358295
       2016  |  -.0095695   .0092753    -1.03   0.302    -.0277649    .0086259
       2017  |  -.0085717   .0093139    -0.92   0.358    -.0268428    .0096994
       2018  |  -.0023429   .0095162    -0.25   0.806    -.0210109    .0163251
       2019  |  -.0071273   .0095619    -0.75   0.456    -.0258851    .0116304
             |
       _cons |  -.0110372   .0293238    -0.38   0.707    -.0685621    .0464878
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.47    0.681418
      exvot1 |      1.93    0.516798
     votsom1 |      1.75    0.571452
      roe_ll |      1.15    0.873021
      tangib |      1.24    0.808196
   lnattotal |      1.53    0.652405
     catset1 |
          2  |      2.16    0.462414
          3  |      1.37    0.730432
          4  |      1.78    0.560666
          5  |      1.85    0.541082
          6  |      1.78    0.562711
          7  |      2.27    0.440852
          8  |      2.66    0.375338
          9  |      1.31    0.762754
         10  |      2.03    0.493819
         11  |      1.92    0.520875
         12  |      2.17    0.460573
         13  |      1.30    0.768507
         98  |      2.87    0.348962
         99  |      1.53    0.652877
        year |
       2011  |      1.88    0.533250
       2012  |      1.91    0.524681
       2013  |      1.95    0.513785
       2014  |      1.98    0.504189
       2015  |      2.03    0.492468
       2016  |      1.99    0.501639
       2017  |      1.97    0.507083
       2018  |      1.97    0.508856
       2019  |      1.90    0.525681
-------------+----------------------
    Mean VIF |      1.85

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   838.43
         Prob > chi2  =   0.0000

. **xtgls   divsubattotal cgindex exvot1 votsom1 roe_ll tangib lnattotal i.catset1 i.ye
> ar if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votso
> m1!=., i(firm_id) t(year)
. 
. ** NÃO INCLUIDO NA TABELA 6
. regress divsubattotal cgindex exvot1 votsom1 vots1_2 roe_ll tangib lnattotal i.catset
> 1 i.year if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. &
>  votsom1!=.

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(30, 1356)     =      4.62
       Model |   .75648288        30  .025216096   Prob > F        =    0.0000
    Residual |  7.40080056     1,356  .005457818   R-squared       =    0.0927
-------------+----------------------------------   Adj R-squared   =    0.0727
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07388

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.0183612   .0238348    -0.77   0.441    -.0651183    .0283959
      exvot1 |   .0238846   .0204386     1.17   0.243    -.0162101    .0639794
     votsom1 |   -.051895   .0343137    -1.51   0.131    -.1192087    .0154187
     vots1_2 |      .0634   .0357733     1.77   0.077    -.0067771     .133577
      roe_ll |   -.004602   .0133995    -0.34   0.731     -.030888     .021684
      tangib |   .0301602   .0132321     2.28   0.023     .0042025    .0561178
   lnattotal |   .0026344   .0016944     1.55   0.120    -.0006896    .0059583
             |
     catset1 |
          2  |   .0243945   .0101511     2.40   0.016     .0044809    .0443082
          3  |  -.0186878   .0144077    -1.30   0.195    -.0469516     .009576
          4  |   .0097638   .0125101     0.78   0.435    -.0147775    .0343052
          5  |   .0005905   .0111823     0.05   0.958    -.0213459    .0225269
          6  |  -.0135913   .0121676    -1.12   0.264    -.0374607    .0102782
          7  |  -.0177547   .0102921    -1.73   0.085    -.0379449    .0024355
          8  |  -.0201027   .0102255    -1.97   0.050    -.0401622   -.0000433
          9  |  -.0143759   .0164539    -0.87   0.382    -.0466538     .017902
         10  |  -.0108399   .0104466    -1.04   0.300    -.0313331    .0096533
         11  |    -.02717   .0114402    -2.37   0.018    -.0496125   -.0047275
         12  |   .0212614   .0103232     2.06   0.040     .0010102    .0415126
         13  |   .0443817   .0155719     2.85   0.004      .013834    .0749294
         98  |  -.0177764   .0096518    -1.84   0.066    -.0367104    .0011576
         99  |  -.0007779   .0138653    -0.06   0.955    -.0279776    .0264218
             |
        year |
       2011  |  -.0018265   .0090186    -0.20   0.840    -.0195184    .0158653
       2012  |   .0060219   .0090367     0.67   0.505    -.0117055    .0237493
       2013  |   .0075372   .0090197     0.84   0.404     -.010157    .0252314
       2014  |   .0130865   .0090762     1.44   0.150    -.0047184    .0308914
       2015  |   .0180254    .009184     1.96   0.050     9.02e-06    .0360417
       2016  |  -.0095581    .009268    -1.03   0.303    -.0277392     .008623
       2017  |  -.0084864   .0093066    -0.91   0.362    -.0267434    .0097705
       2018  |    -.00201   .0095105    -0.21   0.833    -.0206669     .016647
       2019  |  -.0071561   .0095544    -0.75   0.454    -.0258991    .0115869
             |
       _cons |  -.0015803   .0297826    -0.05   0.958    -.0600053    .0568447
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.49    0.672799
      exvot1 |      2.15    0.464694
     votsom1 |     15.96    0.062659
     vots1_2 |     16.78    0.059593
      roe_ll |      1.15    0.872883
      tangib |      1.25    0.802973
   lnattotal |      1.55    0.645824
     catset1 |
          2  |      2.16    0.462359
          3  |      1.37    0.730079
          4  |      1.80    0.554784
          5  |      1.85    0.541079
          6  |      1.78    0.562236
          7  |      2.27    0.440367
          8  |      2.67    0.374907
          9  |      1.31    0.761472
         10  |      2.03    0.493815
         11  |      1.93    0.516952
         12  |      2.17    0.460273
         13  |      1.30    0.766849
         98  |      2.88    0.346632
         99  |      1.53    0.652040
        year |
       2011  |      1.88    0.533120
       2012  |      1.91    0.524349
       2013  |      1.95    0.513551
       2014  |      1.98    0.504140
       2015  |      2.03    0.492375
       2016  |      1.99    0.501638
       2017  |      1.97    0.507070
       2018  |      1.97    0.508658
       2019  |      1.90    0.525679
-------------+----------------------
    Mean VIF |      2.83

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots1_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.033     1    0.0000 |   1 |    1033.033     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   877.21
         Prob > chi2  =   0.0000

. **xtgls   divsubattotal cgindex exvot1 vots1_2 votsom1 roe_ll tangib lnattotal i.cats
> et1 i.year if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=.
>  & votsom1!=., i(firm_id) t(year)
. 
. *************************************************************************************
> **
. **DUMMY NM
. **TABELA 6 - PAINEL B - TESTES
. regress divsubattotal d_nm votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=
> . 

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.76
       Model |  .753774814        29  .025992235   Prob > F        =    0.0000
    Residual |  7.40350862     1,357  .005455791   R-squared       =    0.0924
-------------+----------------------------------   Adj R-squared   =    0.0730
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07386

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0074166   .0055913    -1.33   0.185    -.0183851    .0035519
     votsom1 |  -.0572086   .0335524    -1.71   0.088    -.1230289    .0086116
     vots1_2 |   .0740363   .0338274     2.19   0.029     .0076766    .1403961
      roe_ll |  -.0056052   .0133989    -0.42   0.676      -.03189    .0206795
      tangib |   .0310857   .0131558     2.36   0.018     .0052778    .0568936
   lnattotal |   .0022336   .0016924     1.32   0.187    -.0010865    .0055536
             |
     catset1 |
          2  |   .0246968   .0100787     2.45   0.014     .0049254    .0444683
          3  |  -.0181103   .0143793    -1.26   0.208    -.0463184    .0100977
          4  |   .0093108   .0124575     0.75   0.455    -.0151272    .0337488
          5  |  -.0008329   .0112626    -0.07   0.941    -.0229268     .021261
          6  |  -.0134742   .0121777    -1.11   0.269    -.0373634     .010415
          7  |  -.0171938   .0102635    -1.68   0.094    -.0373278    .0029403
          8  |   -.019769   .0101163    -1.95   0.051    -.0396143    .0000764
          9  |  -.0140626    .016441    -0.86   0.393    -.0463151      .01819
         10  |  -.0121856   .0104331    -1.17   0.243    -.0326524    .0082811
         11  |  -.0286988   .0118163    -2.43   0.015    -.0518791   -.0055186
         12  |   .0205729   .0103547     1.99   0.047       .00026    .0408858
         13  |   .0446618   .0155536     2.87   0.004     .0141502    .0751735
         98  |  -.0183877   .0096399    -1.91   0.057    -.0372984    .0005231
         99  |  -.0007151   .0138853    -0.05   0.959     -.027954    .0265239
             |
        year |
       2011  |  -.0019665   .0090109    -0.22   0.827    -.0196433    .0157104
       2012  |   .0056079   .0090025     0.62   0.533    -.0120525    .0232683
       2013  |   .0067576   .0089421     0.76   0.450    -.0107842    .0242994
       2014  |   .0119703   .0089609     1.34   0.182    -.0056085     .029549
       2015  |   .0166703     .00904     1.84   0.065    -.0010636    .0344043
       2016  |  -.0109313   .0091053    -1.20   0.230    -.0287934    .0069307
       2017  |  -.0099359    .009122    -1.09   0.276    -.0278306    .0079588
       2018  |  -.0037076   .0092118    -0.40   0.687    -.0217786    .0143633
       2019  |  -.0087609   .0093212    -0.94   0.347    -.0270463    .0095246
             |
       _cons |   .0007544   .0300633     0.03   0.980    -.0582212    .0597299
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      1.73    0.578812
     votsom1 |     15.26    0.065511
     vots1_2 |     15.01    0.066621
      roe_ll |      1.15    0.872639
      tangib |      1.23    0.812012
   lnattotal |      1.55    0.647116
     catset1 |
          2  |      2.13    0.468859
          3  |      1.36    0.732696
          4  |      1.79    0.559277
          5  |      1.88    0.533193
          6  |      1.78    0.561096
          7  |      2.26    0.442663
          8  |      2.61    0.382898
          9  |      1.31    0.762386
         10  |      2.02    0.494906
         11  |      2.06    0.484388
         12  |      2.19    0.457309
         13  |      1.30    0.768375
         98  |      2.88    0.347356
         99  |      1.54    0.649918
        year |
       2011  |      1.87    0.533828
       2012  |      1.89    0.528138
       2013  |      1.91    0.522315
       2014  |      1.93    0.516998
       2015  |      1.97    0.507989
       2016  |      1.92    0.519524
       2017  |      1.90    0.527611
       2018  |      1.85    0.541978
       2019  |      1.81    0.552109
-------------+----------------------
    Mean VIF |      2.76

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots1_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.033     1    0.0000 |   1 |    1033.033     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   881.10
         Prob > chi2  =   0.0000

. **xtgls divsubattotal d_nm votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=
> ., i(firm_id) t(year)
. **utest votsom1 vots1_2
. 
. regress divsubattotal d_nm votsom2 vots2_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom2!=
> . 

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.76
       Model |  .753167793        29  .025971303   Prob > F        =    0.0000
    Residual |  7.40411564     1,357  .005456238   R-squared       =    0.0923
-------------+----------------------------------   Adj R-squared   =    0.0729
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07387

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0069625   .0059133    -1.18   0.239    -.0185627    .0046378
     votsom2 |  -.0893709   .0391417    -2.28   0.023    -.1661557   -.0125861
     vots2_2 |   .0872251   .0351642     2.48   0.013     .0182431    .1562072
      roe_ll |  -.0048531   .0133945    -0.36   0.717    -.0311292     .021423
      tangib |   .0306243   .0131641     2.33   0.020     .0048001    .0564486
   lnattotal |   .0019101   .0016927     1.13   0.259    -.0014104    .0052307
             |
     catset1 |
          2  |   .0241438   .0100959     2.39   0.017     .0043386     .043949
          3  |  -.0188461   .0144146    -1.31   0.191    -.0471234    .0094312
          4  |   .0078175   .0124637     0.63   0.531    -.0166327    .0322678
          5  |  -.0024025   .0112952    -0.21   0.832    -.0245604    .0197555
          6  |  -.0152182   .0121407    -1.25   0.210    -.0390349    .0085985
          7  |  -.0188143   .0103242    -1.82   0.069    -.0390674    .0014388
          8  |  -.0212885   .0102348    -2.08   0.038    -.0413662   -.0012109
          9  |  -.0144314   .0164469    -0.88   0.380    -.0466956    .0178327
         10  |  -.0133633   .0104422    -1.28   0.201     -.033848    .0071213
         11  |   -.027203   .0117335    -2.32   0.021    -.0502207   -.0041853
         12  |   .0192398    .010408     1.85   0.065    -.0011777    .0396574
         13  |   .0425378   .0155525     2.74   0.006     .0120282    .0730473
         98  |  -.0199964    .009703    -2.06   0.040     -.039031   -.0009619
         99  |   -.000793   .0139044    -0.06   0.955    -.0280694    .0264833
             |
        year |
       2011  |  -.0017593   .0090124    -0.20   0.845    -.0194391    .0159204
       2012  |   .0053911   .0090014     0.60   0.549     -.012267    .0230493
       2013  |   .0067131   .0089423     0.75   0.453    -.0108292    .0242554
       2014  |   .0120462   .0089611     1.34   0.179    -.0055328    .0296253
       2015  |   .0168612   .0090407     1.87   0.062     -.000874    .0345963
       2016  |  -.0105096   .0091045    -1.15   0.249    -.0283701    .0073509
       2017  |  -.0096095   .0091224    -1.05   0.292     -.027505    .0082859
       2018  |  -.0035269   .0092126    -0.38   0.702    -.0215994    .0145455
       2019  |  -.0089519   .0093264    -0.96   0.337    -.0272477    .0093439
             |
       _cons |   .0171184   .0315306     0.54   0.587    -.0447356    .0789723
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      1.93    0.517528
     votsom2 |     20.84    0.047981
     vots2_2 |     21.59    0.046325
      roe_ll |      1.15    0.873286
      tangib |      1.23    0.811053
   lnattotal |      1.55    0.646967
     catset1 |
          2  |      2.14    0.467301
          3  |      1.37    0.729173
          4  |      1.79    0.558763
          5  |      1.89    0.530159
          6  |      1.77    0.564566
          7  |      2.29    0.437508
          8  |      2.67    0.374119
          9  |      1.31    0.761900
         10  |      2.02    0.494083
         11  |      2.04    0.491294
         12  |      2.21    0.452670
         13  |      1.30    0.768543
         98  |      2.92    0.342882
         99  |      1.54    0.648191
        year |
       2011  |      1.87    0.533697
       2012  |      1.89    0.528313
       2013  |      1.91    0.522329
       2014  |      1.93    0.517026
       2015  |      1.97    0.507962
       2016  |      1.92    0.519658
       2017  |      1.90    0.527612
       2018  |      1.85    0.541934
       2019  |      1.81    0.551531
-------------+----------------------
    Mean VIF |      3.19

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1073.819     1    0.0000 |   1 |    1073.819     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots2_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1073.196     1    0.0000 |   1 |    1073.196     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   855.94
         Prob > chi2  =   0.0000

. **xtgls divsubattotal d_nm votsom2 vots2_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom2!=
> ., i(firm_id) t(year)
. **utest votsom2 vots2_2
. 
. regress divsubattotal d_nm votsom3 vots3_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom3!=
> . 

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.75
       Model |  .751598536        29  .025917191   Prob > F        =    0.0000
    Residual |   7.4056849     1,357  .005457395   R-squared       =    0.0921
-------------+----------------------------------   Adj R-squared   =    0.0727
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07387

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0063664   .0060909    -1.05   0.296    -.0183149    .0055821
     votsom3 |  -.1007511    .043934    -2.29   0.022     -.186937   -.0145652
     vots3_2 |    .091281   .0374513     2.44   0.015     .0178123    .1647497
      roe_ll |  -.0047242   .0134004    -0.35   0.724    -.0310121    .0215636
      tangib |   .0318873   .0131134     2.43   0.015     .0061626    .0576121
   lnattotal |   .0018241   .0016996     1.07   0.283    -.0015099    .0051581
             |
     catset1 |
          2  |    .024373   .0100903     2.42   0.016     .0045788    .0441673
          3  |  -.0183859   .0144184    -1.28   0.202    -.0466706    .0098988
          4  |   .0080037   .0124753     0.64   0.521    -.0164692    .0324766
          5  |  -.0026528   .0113197    -0.23   0.815    -.0248589    .0195533
          6  |  -.0153954   .0121168    -1.27   0.204     -.039165    .0083742
          7  |  -.0189097   .0103375    -1.83   0.068    -.0391888    .0013695
          8  |  -.0204253   .0101793    -2.01   0.045    -.0403942   -.0004564
          9  |  -.0150488   .0164556    -0.91   0.361    -.0473299    .0172323
         10  |  -.0138503    .010456    -1.32   0.186    -.0343619    .0066613
         11  |   -.026199   .0117274    -2.23   0.026    -.0492049   -.0031931
         12  |   .0191907   .0104319     1.84   0.066    -.0012737     .039655
         13  |   .0432994   .0155225     2.79   0.005     .0128487      .07375
         98  |  -.0199632   .0097064    -2.06   0.040    -.0390043   -.0009221
         99  |  -.0007213   .0139126    -0.05   0.959    -.0280138    .0265713
             |
        year |
       2011  |  -.0016504   .0090153    -0.18   0.855    -.0193358     .016035
       2012  |   .0054169   .0090025     0.60   0.547    -.0122434    .0230772
       2013  |   .0068913   .0089441     0.77   0.441    -.0106544     .024437
       2014  |   .0121729   .0089623     1.36   0.175    -.0054085    .0297543
       2015  |   .0171992   .0090437     1.90   0.057    -.0005419    .0349403
       2016  |  -.0103257   .0091062    -1.13   0.257    -.0281894     .007538
       2017  |  -.0094627   .0091244    -1.04   0.300    -.0273621    .0084367
       2018  |  -.0034066   .0092146    -0.37   0.712     -.021483    .0146698
       2019  |   -.009032   .0093357    -0.97   0.333    -.0273459    .0092819
             |
       _cons |   .0225087   .0328481     0.69   0.493    -.0419298    .0869472
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.05    0.487899
     votsom3 |     23.95    0.041752
     vots3_2 |     25.10    0.039834
      roe_ll |      1.15    0.872693
      tangib |      1.22    0.817513
   lnattotal |      1.56    0.641877
     catset1 |
          2  |      2.14    0.467916
          3  |      1.37    0.728944
          4  |      1.79    0.557846
          5  |      1.89    0.527975
          6  |      1.76    0.566923
          7  |      2.29    0.436479
          8  |      2.64    0.378285
          9  |      1.31    0.761261
         10  |      2.03    0.492890
         11  |      2.03    0.491904
         12  |      2.22    0.450700
         13  |      1.30    0.771684
         98  |      2.92    0.342718
         99  |      1.54    0.647559
        year |
       2011  |      1.87    0.533471
       2012  |      1.89    0.528299
       2013  |      1.91    0.522236
       2014  |      1.93    0.516995
       2015  |      1.97    0.507728
       2016  |      1.92    0.519581
       2017  |      1.90    0.527490
       2018  |      1.85    0.541810
       2019  |      1.82    0.550557
-------------+----------------------
    Mean VIF |      3.43

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom3

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1053.856     1    0.0000 |   1 |    1053.856     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots3_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1060.826     1    0.0000 |   1 |    1060.826     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   854.64
         Prob > chi2  =   0.0000

. **xtgls divsubattotal d_nm votsom3 vots3_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom3!=
> ., i(firm_id) t(year)
. **utest votsom3 vots3_2
. 
. regress divsubattotal d_nm votsom4 vots4_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom4!=
> . 

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.70
       Model |  .744174522        29   .02566119   Prob > F        =    0.0000
    Residual |  7.41310891     1,357  .005462866   R-squared       =    0.0912
-------------+----------------------------------   Adj R-squared   =    0.0718
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07391

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0069351   .0061898    -1.12   0.263    -.0190776    .0052075
     votsom4 |  -.0992236   .0476441    -2.08   0.037    -.1926878   -.0057594
     vots4_2 |   .0851238   .0395987     2.15   0.032     .0074424    .1628052
      roe_ll |  -.0047759   .0134176    -0.36   0.722    -.0310974    .0215456
      tangib |   .0329112     .01309     2.51   0.012     .0072324      .05859
   lnattotal |   .0017595   .0017065     1.03   0.303    -.0015882    .0051072
             |
     catset1 |
          2  |   .0247352   .0100912     2.45   0.014     .0049392    .0445311
          3  |  -.0188367   .0144359    -1.30   0.192    -.0471559    .0094824
          4  |   .0083061   .0124732     0.67   0.506    -.0161628    .0327749
          5  |  -.0026064   .0113177    -0.23   0.818    -.0248085    .0195958
          6  |  -.0159157   .0121271    -1.31   0.190    -.0397056    .0078742
          7  |  -.0188968   .0103358    -1.83   0.068    -.0391728    .0013791
          8  |  -.0196615   .0101786    -1.93   0.054    -.0396291     .000306
          9  |  -.0143295   .0164558    -0.87   0.384    -.0466111    .0179521
         10  |  -.0141403   .0104815    -1.35   0.178     -.034702    .0064213
         11  |  -.0261884   .0117248    -2.23   0.026     -.049189   -.0031877
         12  |   .0195114   .0104163     1.87   0.061    -.0009223    .0399452
         13  |   .0436321   .0155262     2.81   0.005     .0131741    .0740902
         98  |  -.0197851   .0096928    -2.04   0.041    -.0387997   -.0007705
         99  |  -.0011868   .0139057    -0.09   0.932    -.0284659    .0260923
             |
        year |
       2011  |  -.0017014   .0090209    -0.19   0.850    -.0193979    .0159951
       2012  |   .0052893   .0090066     0.59   0.557     -.012379    .0229576
       2013  |   .0069197   .0089495     0.77   0.440    -.0106366     .024476
       2014  |   .0120794   .0089669     1.35   0.178    -.0055111    .0296699
       2015  |   .0172301   .0090496     1.90   0.057    -.0005227    .0349828
       2016  |  -.0104718   .0091111    -1.15   0.251    -.0283452    .0074015
       2017  |  -.0095454   .0091287    -1.05   0.296    -.0274532    .0083624
       2018  |  -.0035265   .0092187    -0.38   0.702    -.0216109    .0145579
       2019  |  -.0092295   .0093459    -0.99   0.324    -.0275634    .0091045
             |
       _cons |   .0253752   .0337843     0.75   0.453    -.0408998    .0916503
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.11    0.472906
     votsom4 |     25.94    0.038557
     vots4_2 |     27.44    0.036440
      roe_ll |      1.15    0.871336
      tangib |      1.22    0.821263
   lnattotal |      1.57    0.637283
     catset1 |
          2  |      2.14    0.468305
          3  |      1.37    0.727902
          4  |      1.79    0.558593
          5  |      1.89    0.528692
          6  |      1.77    0.566524
          7  |      2.29    0.437054
          8  |      2.64    0.378715
          9  |      1.31    0.762002
         10  |      2.04    0.490985
         11  |      2.03    0.492620
         12  |      2.21    0.452503
         13  |      1.30    0.772082
         98  |      2.91    0.344019
         99  |      1.54    0.648849
        year |
       2011  |      1.87    0.533335
       2012  |      1.89    0.528350
       2013  |      1.92    0.522129
       2014  |      1.93    0.516979
       2015  |      1.97    0.507571
       2016  |      1.92    0.519540
       2017  |      1.90    0.527523
       2018  |      1.85    0.541875
       2019  |      1.82    0.549904
-------------+----------------------
    Mean VIF |      3.58

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom4

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.489     1    0.0000 |   1 |    1033.489     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots4_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1047.969     1    0.0000 |   1 |    1047.969     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   846.09
         Prob > chi2  =   0.0000

. **xtgls divsubattotal d_nm votsom4 vots4_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom4!=
> ., i(firm_id) t(year)
. **utest votsom4 vots4_2
. 
. regress divsubattotal d_nm votsom5 vots5_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom5!=
> . 

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.64
       Model |  .735488103        29  .025361659   Prob > F        =    0.0000
    Residual |  7.42179533     1,357  .005469267   R-squared       =    0.0902
-------------+----------------------------------   Adj R-squared   =    0.0707
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07395

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0072312   .0062367    -1.16   0.246    -.0194657    .0050034
     votsom5 |  -.0835569   .0496808    -1.68   0.093    -.1810164    .0139027
     vots5_2 |   .0711422    .040885     1.74   0.082    -.0090624    .1513468
      roe_ll |  -.0049308   .0134307    -0.37   0.714    -.0312779    .0214163
      tangib |   .0337721   .0130794     2.58   0.010     .0081141    .0594301
   lnattotal |    .001809   .0017114     1.06   0.291    -.0015482    .0051663
             |
     catset1 |
          2  |   .0250527   .0100947     2.48   0.013     .0052498    .0448556
          3  |  -.0186387   .0144418    -1.29   0.197    -.0469695     .009692
          4  |   .0093588   .0124521     0.75   0.452    -.0150686    .0337862
          5  |  -.0019057   .0113093    -0.17   0.866    -.0240914      .02028
          6  |  -.0155426   .0121347    -1.28   0.200    -.0393473    .0082621
          7  |  -.0183603   .0103272    -1.78   0.076    -.0386193    .0018987
          8  |  -.0189565   .0101747    -1.86   0.063    -.0389164    .0010034
          9  |   -.014004   .0164622    -0.85   0.395    -.0462981    .0182901
         10  |  -.0136693   .0104835    -1.30   0.192    -.0342348    .0068962
         11  |  -.0259604   .0117254    -2.21   0.027    -.0489622   -.0029585
         12  |    .020186   .0103988     1.94   0.052    -.0002135    .0405855
         13  |   .0439863   .0155329     2.83   0.005     .0135153    .0744573
         98  |  -.0190908   .0096733    -1.97   0.049     -.038067   -.0001146
         99  |  -.0014778   .0139009    -0.11   0.915    -.0287474    .0257918
             |
        year |
       2011  |   -.001828    .009026    -0.20   0.840    -.0195344    .0158784
       2012  |   .0051804   .0090116     0.57   0.565    -.0124977    .0228585
       2013  |   .0067835   .0089543     0.76   0.449    -.0107824    .0243494
       2014  |   .0120095   .0089728     1.34   0.181    -.0055925    .0296116
       2015  |   .0171056   .0090557     1.89   0.059    -.0006591    .0348703
       2016  |  -.0105619   .0091173    -1.16   0.247    -.0284475    .0073237
       2017  |  -.0095975   .0091338    -1.05   0.294    -.0275153    .0083204
       2018  |  -.0035777   .0092238    -0.39   0.698    -.0216721    .0145168
       2019  |  -.0091206   .0093539    -0.98   0.330    -.0274703    .0092292
             |
       _cons |   .0207213   .0341918     0.61   0.545    -.0463532    .0877958
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.14    0.466365
     votsom5 |     26.54    0.037682
     vots5_2 |     28.30    0.035334
      roe_ll |      1.15    0.870659
      tangib |      1.21    0.823559
   lnattotal |      1.58    0.634398
     catset1 |
          2  |      2.13    0.468526
          3  |      1.37    0.728157
          4  |      1.78    0.561144
          5  |      1.89    0.530097
          6  |      1.77    0.566482
          7  |      2.28    0.438300
          8  |      2.64    0.379450
          9  |      1.31    0.762304
         10  |      2.04    0.491375
         11  |      2.03    0.493147
         12  |      2.20    0.454557
         13  |      1.29    0.772328
         98  |      2.89    0.345816
         99  |      1.54    0.650060
        year |
       2011  |      1.87    0.533362
       2012  |      1.89    0.528383
       2013  |      1.92    0.522172
       2014  |      1.93    0.516908
       2015  |      1.97    0.507484
       2016  |      1.93    0.519437
       2017  |      1.90    0.527550
       2018  |      1.85    0.541907
       2019  |      1.82    0.549600
-------------+----------------------
    Mean VIF |      3.63

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom5

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1015.172     1    0.0000 |   1 |    1015.172     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots5_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1036.555     1    0.0000 |   1 |    1036.555     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   836.09
         Prob > chi2  =   0.0000

. **xtgls divsubattotal d_nm votsom5 vots5_2 roe_ll tangib lnattotal i.catset1 i.year i
> f divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom5!=
> ., i(firm_id) t(year)
. **utest votsom5 vots5_2
. 
. ** EXVOT 1
. regress divsubattotal d_nm exvot1 roe_ll tangib lnattotal i.catset1 i.year if divsuba
> ttotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=.

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(28, 1358)     =      4.82
       Model |  .736719965        28  .026311427   Prob > F        =    0.0000
    Residual |  7.42056347     1,358  .005464332   R-squared       =    0.0903
-------------+----------------------------------   Adj R-squared   =    0.0716
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07392

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0034524   .0063826    -0.54   0.589    -.0159732    .0090684
      exvot1 |   .0367706   .0203779     1.80   0.071    -.0032049    .0767461
      roe_ll |  -.0050779   .0134038    -0.38   0.705    -.0313723    .0212166
      tangib |   .0316768   .0131836     2.40   0.016     .0058143    .0575393
   lnattotal |   .0018846   .0016768     1.12   0.261    -.0014048    .0051741
             |
     catset1 |
          2  |   .0236275   .0101653     2.32   0.020     .0036862    .0435688
          3  |  -.0192493   .0142922    -1.35   0.178    -.0472865    .0087878
          4  |   .0104817   .0123012     0.85   0.394    -.0136497     .034613
          5  |    -.00128   .0112245    -0.11   0.909    -.0232992    .0207393
          6  |  -.0137949   .0120917    -1.14   0.254    -.0375154    .0099256
          7  |  -.0177102   .0102467    -1.73   0.084    -.0378112    .0023909
          8  |  -.0202781    .010264    -1.98   0.048    -.0404132    -.000143
          9  |  -.0157802   .0164936    -0.96   0.339    -.0481358    .0165755
         10  |  -.0117006   .0104389    -1.12   0.263    -.0321787    .0087776
         11  |  -.0269841   .0117142    -2.30   0.021     -.049964   -.0040042
         12  |   .0200325   .0103085     1.94   0.052    -.0001899    .0402548
         13  |   .0435131    .015538     2.80   0.005     .0130321    .0739941
         98  |  -.0177783   .0095164    -1.87   0.062    -.0364468    .0008902
         99  |  -.0023132   .0138772    -0.17   0.868    -.0295364      .02491
             |
        year |
       2011  |  -.0021655   .0090168    -0.24   0.810    -.0198538    .0155229
       2012  |   .0051635   .0090074     0.57   0.567    -.0125064    .0228335
       2013  |   .0064986   .0089478     0.73   0.468    -.0110544    .0240516
       2014  |   .0121039   .0089679     1.35   0.177    -.0054886    .0296964
       2015  |   .0169005   .0090476     1.87   0.062    -.0008482    .0346493
       2016  |  -.0104692   .0091109    -1.15   0.251    -.0283421    .0074038
       2017  |  -.0095847    .009129    -1.05   0.294    -.0274932    .0083238
       2018  |  -.0036894   .0092182    -0.40   0.689    -.0217729    .0143941
       2019  |  -.0084932   .0093194    -0.91   0.362    -.0267751    .0097888
             |
       _cons |  -.0065417   .0282095    -0.23   0.817    -.0618806    .0487971
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.25    0.444883
      exvot1 |      2.14    0.468027
      roe_ll |      1.15    0.873360
      tangib |      1.23    0.809855
   lnattotal |      1.51    0.660219
     catset1 |
          2  |      2.17    0.461627
          3  |      1.35    0.742814
          4  |      1.74    0.574479
          5  |      1.86    0.537655
          6  |      1.75    0.569997
          7  |      2.25    0.444812
          8  |      2.68    0.372539
          9  |      1.32    0.758720
         10  |      2.02    0.495130
         11  |      2.03    0.493642
         12  |      2.16    0.462136
         13  |      1.30    0.771125
         98  |      2.80    0.356986
         99  |      1.53    0.651691
        year |
       2011  |      1.87    0.533967
       2012  |      1.89    0.528393
       2013  |      1.91    0.522463
       2014  |      1.93    0.517000
       2015  |      1.97    0.507936
       2016  |      1.92    0.519701
       2017  |      1.90    0.527624
       2018  |      1.84    0.542072
       2019  |      1.81    0.553185
-------------+----------------------
    Mean VIF |      1.87

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   833.67
         Prob > chi2  =   0.0000

. **xtgls   divsubattotal d_nm exvot1 roe_ll tangib lnattotal i.catset1 i.year if divsu
> battotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=., i(fi
> rm_id) t(year)
. 
. regress divsubattotal d_nm exvot1 votsom1 roe_ll tangib lnattotal i.catset1 i.year if
>  divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=.

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(29, 1357)     =      4.66
       Model |  .738732383        29   .02547353   Prob > F        =    0.0000
    Residual |  7.41855105     1,357  .005466876   R-squared       =    0.0906
-------------+----------------------------------   Adj R-squared   =    0.0711
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07394

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0030043   .0064267    -0.47   0.640    -.0156115     .009603
      exvot1 |   .0315312   .0221365     1.42   0.155    -.0118943    .0749567
     votsom1 |   .0066809   .0110115     0.61   0.544    -.0149206    .0282824
      roe_ll |  -.0053955   .0134172    -0.40   0.688    -.0317161    .0209252
      tangib |   .0320581   .0132017     2.43   0.015     .0061602     .057956
   lnattotal |   .0020273   .0016936     1.20   0.232    -.0012951    .0053497
             |
     catset1 |
          2  |   .0238038   .0101718     2.34   0.019     .0038497    .0437579
          3  |  -.0181159   .0144171    -1.26   0.209    -.0463981    .0101662
          4  |    .011436   .0124042     0.92   0.357    -.0128974    .0357694
          5  |  -.0006067   .0112818    -0.05   0.957    -.0227384     .021525
          6  |  -.0128897   .0121862    -1.06   0.290    -.0367955    .0110162
          7  |  -.0171191   .0102953    -1.66   0.097    -.0373154    .0030773
          8  |   -.020065   .0102724    -1.95   0.051    -.0402166    .0000866
          9  |  -.0158755   .0164982    -0.96   0.336    -.0482402    .0164891
         10  |  -.0115999   .0104427    -1.11   0.267    -.0320854    .0088856
         11  |  -.0264468   .0117504    -2.25   0.025    -.0494976    -.003396
         12  |   .0208526   .0103992     2.01   0.045     .0004524    .0412528
         13  |   .0429494   .0155693     2.76   0.006     .0124068    .0734919
         98  |  -.0169685   .0096118    -1.77   0.078     -.035824    .0018871
         99  |  -.0017873   .0139075    -0.13   0.898    -.0290699    .0254953
             |
        year |
       2011  |  -.0022152   .0090193    -0.25   0.806    -.0199084     .015478
       2012  |   .0052468   .0090105     0.58   0.560    -.0124293    .0229229
       2013  |   .0065803   .0089509     0.74   0.462    -.0109788    .0241394
       2014  |   .0121394   .0089702     1.35   0.176    -.0054575    .0297364
       2015  |   .0168665   .0090499     1.86   0.063    -.0008867    .0346198
       2016  |  -.0105285   .0091135    -1.16   0.248    -.0284067    .0073497
       2017  |  -.0095807   .0091311    -1.05   0.294    -.0274934     .008332
       2018  |  -.0036224    .009221    -0.39   0.695    -.0217114    .0144667
       2019  |  -.0082851   .0093279    -0.89   0.375    -.0265836    .0100135
             |
       _cons |  -.0118552   .0295439    -0.40   0.688    -.0698118    .0461015
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.28    0.439006
      exvot1 |      2.52    0.396800
     votsom1 |      1.64    0.609460
      roe_ll |      1.15    0.872030
      tangib |      1.24    0.808019
   lnattotal |      1.54    0.647491
     catset1 |
          2  |      2.17    0.461250
          3  |      1.37    0.730343
          4  |      1.77    0.565241
          5  |      1.88    0.532453
          6  |      1.78    0.561453
          7  |      2.27    0.440828
          8  |      2.69    0.372103
          9  |      1.32    0.758651
         10  |      2.02    0.495005
         11  |      2.04    0.490838
         12  |      2.20    0.454328
         13  |      1.30    0.768379
         98  |      2.86    0.350102
         99  |      1.54    0.649160
        year |
       2011  |      1.87    0.533923
       2012  |      1.89    0.528271
       2013  |      1.91    0.522345
       2014  |      1.93    0.516978
       2015  |      1.97    0.507916
       2016  |      1.92    0.519641
       2017  |      1.90    0.527624
       2018  |      1.85    0.541994
       2019  |      1.81    0.552437
-------------+----------------------
    Mean VIF |      1.88

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   840.22
         Prob > chi2  =   0.0000

. **xtgls   divsubattotal d_nm exvot1 votsom1 roe_ll tangib lnattotal i.catset1 i.year 
> if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!
> =., i(firm_id) t(year)
. 
. ** NÃO INCLUIDO NA TABELA 6
. regress divsubattotal d_nm exvot1 votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i
> .year if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & vo
> tsom1!=.

      Source |       SS           df       MS      Number of obs   =     1,387
-------------+----------------------------------   F(30, 1356)     =      4.62
       Model |  .756509944        30  .025216998   Prob > F        =    0.0000
    Residual |  7.40077349     1,356  .005457798   R-squared       =    0.0927
-------------+----------------------------------   Adj R-squared   =    0.0727
       Total |  8.15728344     1,386  .005885486   Root MSE        =    .07388

------------------------------------------------------------------------------
divsubatto~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0050436     .00652    -0.77   0.439     -.017834    .0077467
      exvot1 |   .0167031   .0235948     0.71   0.479    -.0295832    .0629893
     votsom1 |  -.0520371   .0343445    -1.52   0.130    -.1194113    .0153371
     vots1_2 |   .0651394   .0360925     1.80   0.071    -.0056637    .1359425
      roe_ll |  -.0053541    .013406    -0.40   0.690    -.0316529    .0209447
      tangib |   .0300663   .0132368     2.27   0.023     .0040995    .0560331
   lnattotal |   .0021807   .0016944     1.29   0.198    -.0011431    .0055046
             |
     catset1 |
          2  |   .0237802   .0101633     2.34   0.019     .0038426    .0437178
          3  |  -.0187375   .0144092    -1.30   0.194    -.0470042    .0095293
          4  |   .0089033   .0124731     0.71   0.475    -.0155653    .0333719
          5  |  -.0012137   .0112775    -0.11   0.914    -.0233369    .0209095
          6  |  -.0137133   .0121846    -1.13   0.261    -.0376161    .0101895
          7  |  -.0177181   .0102921    -1.72   0.085    -.0379083     .002472
          8  |  -.0210485   .0102784    -2.05   0.041    -.0412118   -.0008853
          9  |  -.0149555   .0164923    -0.91   0.365    -.0473087    .0173978
         10  |  -.0120456   .0104369    -1.15   0.249    -.0325198    .0084287
         11  |  -.0290562   .0118293    -2.46   0.014    -.0522619   -.0058505
         12  |   .0198635    .010405     1.91   0.056    -.0005481     .040275
         13  |   .0441798   .0155713     2.84   0.005     .0136334    .0747263
         98  |  -.0186785   .0096504    -1.94   0.053    -.0376099    .0002529
         99  |  -.0011369   .0139006    -0.08   0.935     -.028406    .0261321
             |
        year |
       2011  |  -.0019898   .0090126    -0.22   0.825      -.01967    .0156904
       2012  |   .0055473   .0090046     0.62   0.538    -.0121172    .0232117
       2013  |   .0067263   .0089438     0.75   0.452     -.010819    .0242715
       2014  |    .012029    .008963     1.34   0.180    -.0055538    .0296118
       2015  |   .0167579   .0090426     1.85   0.064    -.0009811    .0344968
       2016  |  -.0108502   .0091077    -1.19   0.234    -.0287169    .0070166
       2017  |  -.0098396   .0091247    -1.08   0.281    -.0277396    .0080604
       2018  |  -.0037151   .0092135    -0.40   0.687    -.0217895    .0143592
       2019  |   -.008713   .0093231    -0.93   0.350    -.0270023    .0095763
             |
       _cons |  -.0007967   .0301485    -0.03   0.979    -.0599396    .0583461
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.35    0.425820
      exvot1 |      2.87    0.348688
     votsom1 |     15.99    0.062547
     vots1_2 |     17.08    0.058543
      roe_ll |      1.15    0.872028
      tangib |      1.25    0.802402
   lnattotal |      1.55    0.645861
     catset1 |
          2  |      2.17    0.461249
          3  |      1.37    0.729926
          4  |      1.79    0.558086
          5  |      1.88    0.531979
          6  |      1.78    0.560665
          7  |      2.27    0.440370
          8  |      2.70    0.371057
          9  |      1.32    0.757926
         10  |      2.02    0.494728
         11  |      2.07    0.483506
         12  |      2.21    0.453067
         13  |      1.30    0.766906
         98  |      2.88    0.346727
         99  |      1.54    0.648723
        year |
       2011  |      1.87    0.533821
       2012  |      1.89    0.528090
       2013  |      1.91    0.522302
       2014  |      1.93    0.516954
       2015  |      1.97    0.507894
       2016  |      1.93    0.519442
       2017  |      1.90    0.527494
       2018  |      1.85    0.541977
       2019  |      1.81    0.552080
-------------+----------------------
    Mean VIF |      2.89

. actest divsubattotal

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |     17.982      1    0.0000 |   1 |     17.982      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots1_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.033     1    0.0000 |   1 |    1033.033     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of divsubattotal

         chi2(1)      =   879.94
         Prob > chi2  =   0.0000

. **xtgls divsubattotal d_nm exvot1 votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i
> .year if divsubattotal !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & vo
> tsom1!=., i(firm_id) t(year)
. 

. ************************************************************************************
> ****
. **CG INDEX
. **TABELA 7 - PAINEIS A E B - TESTES

. regress dum_capsub cgindex votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.year 
> if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.35
       Model |  24.2210778    29  .835209579           Prob > F      =  0.0000
    Residual |   109.55686  1357  .080734606           R-squared     =  0.1811
-------------+------------------------------           Adj R-squared =  0.1636
       Total |  133.777938  1386  .096520879           Root MSE      =  .28414

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.1039965   .0907699    -1.15   0.252    -.2820611    .0740681
     votsom1 |  -.0949002   .1303969    -0.73   0.467    -.3507016    .1609011
     vots1_2 |   .1347842   .1304677     1.03   0.302     -.121156    .3907244
      roe_ll |  -.0517081   .0515358    -1.00   0.316    -.1528065    .0493903
      tangib |   .2788425   .0503405     5.54   0.000     .1800888    .3775961
   lnattotal |   .0346772   .0064129     5.41   0.000      .022097    .0472574
             |
     catset1 |
          2  |   .1016242   .0383834     2.65   0.008     .0263269    .1769214
          3  |  -.0612775   .0552202    -1.11   0.267    -.1696037    .0470486
          4  |  -.0091929   .0479945    -0.19   0.848    -.1033443    .0849585
          5  |   .0102405   .0425169     0.24   0.810    -.0731654    .0936465
          6  |  -.0258297   .0466191    -0.55   0.580    -.1172831    .0656237
          7  |  -.0885276   .0394523    -2.24   0.025    -.1659217   -.0111335
          8  |  -.0996265   .0378939    -2.63   0.009    -.1739635   -.0252896
          9  |  -.1104093   .0627586    -1.76   0.079    -.2335237    .0127052
         10  |  -.0496947   .0401602    -1.24   0.216    -.1284776    .0290882
         11  |  -.0680668   .0430868    -1.58   0.114    -.1525906    .0164571
         12  |   .0450282   .0389268     1.16   0.248     -.031335    .1213914
         13  |   .3003668   .0597984     5.02   0.000     .1830594    .4176742
         98  |  -.0736343   .0369607    -1.99   0.047    -.1461406    -.001128
         99  |   .0121693   .0530772     0.23   0.819     -.091953    .1162915
             |
        year |
       2011  |  -.0023008   .0346863    -0.07   0.947    -.0703455    .0657438
       2012  |   .0260862   .0347532     0.75   0.453    -.0420897    .0942622
       2013  |   .0192193   .0346864     0.55   0.580    -.0488254    .0872641
       2014  |   .0007653   .0349068     0.02   0.983    -.0677118    .0692424
       2015  |  -.0090273   .0353201    -0.26   0.798    -.0783152    .0602607
       2016  |   -.106607    .035644    -2.99   0.003    -.1765303   -.0366838
       2017  |  -.0880956   .0357937    -2.46   0.014    -.1583125   -.0178786
       2018  |  -.0828564   .0365653    -2.27   0.024    -.1545871   -.0111257
       2019  |  -.1120231   .0367411    -3.05   0.002    -.1840986   -.0399477
             |
       _cons |  -.2878807   .1139777    -2.53   0.012    -.5114724   -.0642891
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.46    0.686223
     votsom1 |     15.58    0.064184
     vots1_2 |     15.09    0.066274
      roe_ll |      1.15    0.872884
      tangib |      1.22    0.820662
   lnattotal |      1.50    0.666948
     catset1 |
          2  |      2.09    0.478370
          3  |      1.36    0.735200
          4  |      1.79    0.557580
          5  |      1.81    0.553653
          6  |      1.77    0.566556
          7  |      2.26    0.443324
          8  |      2.48    0.403825
          9  |      1.29    0.774260
         10  |      2.02    0.494265
         11  |      1.85    0.539104
         12  |      2.09    0.478838
         13  |      1.30    0.769229
         98  |      2.86    0.349658
         99  |      1.52    0.658196
        year |
       2011  |      1.88    0.533120
       2012  |      1.91    0.524430
       2013  |      1.95    0.513681
       2014  |      1.98    0.504170
       2015  |      2.03    0.492439
       2016  |      1.99    0.501680
       2017  |      1.97    0.507087
       2018  |      1.96    0.509022
       2019  |      1.90    0.525852
-------------+----------------------
    Mean VIF |      2.76

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots1_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.033     1    0.0000 |   1 |    1033.033     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   541.19
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub cgindex votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!
> =., re nolog vce (robust)
. 
. regress dum_capsub cgindex votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.year 
> if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.35
       Model |  24.2210778    29  .835209579           Prob > F      =  0.0000
    Residual |   109.55686  1357  .080734606           R-squared     =  0.1811
-------------+------------------------------           Adj R-squared =  0.1636
       Total |  133.777938  1386  .096520879           Root MSE      =  .28414

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.1039965   .0907699    -1.15   0.252    -.2820611    .0740681
     votsom1 |  -.0949002   .1303969    -0.73   0.467    -.3507016    .1609011
     vots1_2 |   .1347842   .1304677     1.03   0.302     -.121156    .3907244
      roe_ll |  -.0517081   .0515358    -1.00   0.316    -.1528065    .0493903
      tangib |   .2788425   .0503405     5.54   0.000     .1800888    .3775961
   lnattotal |   .0346772   .0064129     5.41   0.000      .022097    .0472574
             |
     catset1 |
          2  |   .1016242   .0383834     2.65   0.008     .0263269    .1769214
          3  |  -.0612775   .0552202    -1.11   0.267    -.1696037    .0470486
          4  |  -.0091929   .0479945    -0.19   0.848    -.1033443    .0849585
          5  |   .0102405   .0425169     0.24   0.810    -.0731654    .0936465
          6  |  -.0258297   .0466191    -0.55   0.580    -.1172831    .0656237
          7  |  -.0885276   .0394523    -2.24   0.025    -.1659217   -.0111335
          8  |  -.0996265   .0378939    -2.63   0.009    -.1739635   -.0252896
          9  |  -.1104093   .0627586    -1.76   0.079    -.2335237    .0127052
         10  |  -.0496947   .0401602    -1.24   0.216    -.1284776    .0290882
         11  |  -.0680668   .0430868    -1.58   0.114    -.1525906    .0164571
         12  |   .0450282   .0389268     1.16   0.248     -.031335    .1213914
         13  |   .3003668   .0597984     5.02   0.000     .1830594    .4176742
         98  |  -.0736343   .0369607    -1.99   0.047    -.1461406    -.001128
         99  |   .0121693   .0530772     0.23   0.819     -.091953    .1162915
             |
        year |
       2011  |  -.0023008   .0346863    -0.07   0.947    -.0703455    .0657438
       2012  |   .0260862   .0347532     0.75   0.453    -.0420897    .0942622
       2013  |   .0192193   .0346864     0.55   0.580    -.0488254    .0872641
       2014  |   .0007653   .0349068     0.02   0.983    -.0677118    .0692424
       2015  |  -.0090273   .0353201    -0.26   0.798    -.0783152    .0602607
       2016  |   -.106607    .035644    -2.99   0.003    -.1765303   -.0366838
       2017  |  -.0880956   .0357937    -2.46   0.014    -.1583125   -.0178786
       2018  |  -.0828564   .0365653    -2.27   0.024    -.1545871   -.0111257
       2019  |  -.1120231   .0367411    -3.05   0.002    -.1840986   -.0399477
             |
       _cons |  -.2878807   .1139777    -2.53   0.012    -.5114724   -.0642891
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.46    0.686223
     votsom1 |     15.58    0.064184
     vots1_2 |     15.09    0.066274
      roe_ll |      1.15    0.872884
      tangib |      1.22    0.820662
   lnattotal |      1.50    0.666948
     catset1 |
          2  |      2.09    0.478370
          3  |      1.36    0.735200
          4  |      1.79    0.557580
          5  |      1.81    0.553653
          6  |      1.77    0.566556
          7  |      2.26    0.443324
          8  |      2.48    0.403825
          9  |      1.29    0.774260
         10  |      2.02    0.494265
         11  |      1.85    0.539104
         12  |      2.09    0.478838
         13  |      1.30    0.769229
         98  |      2.86    0.349658
         99  |      1.52    0.658196
        year |
       2011  |      1.88    0.533120
       2012  |      1.91    0.524430
       2013  |      1.95    0.513681
       2014  |      1.98    0.504170
       2015  |      2.03    0.492439
       2016  |      1.99    0.501680
       2017  |      1.97    0.507087
       2018  |      1.96    0.509022
       2019  |      1.90    0.525852
-------------+----------------------
    Mean VIF |      2.76

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1073.819     1    0.0000 |   1 |    1073.819     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots2_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1073.196     1    0.0000 |   1 |    1073.196     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   541.19
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub cgindex votsom2 vots2_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom2!
> =., re nolog vce (robust)
. 
. regress dum_capsub cgindex votsom3 vots3_2 roe_ll tangib lnattotal i.catset1 i.year 
> if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom3!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.39
       Model |  24.3138537    29  .838408749           Prob > F      =  0.0000
    Residual |  109.464084  1357  .080666237           R-squared     =  0.1817
-------------+------------------------------           Adj R-squared =  0.1643
       Total |  133.777938  1386  .096520879           Root MSE      =  .28402

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |   -.112677   .0928988    -1.21   0.225    -.2949178    .0695637
     votsom3 |  -.2736711   .1686897    -1.62   0.105     -.604592    .0572499
     vots3_2 |   .2418628   .1407376     1.72   0.086     -.034224    .5179496
      roe_ll |  -.0494673   .0514362    -0.96   0.336    -.1503704    .0514359
      tangib |   .2768493   .0502077     5.51   0.000     .1783561    .3753425
   lnattotal |   .0335886   .0064196     5.23   0.000     .0209951    .0461821
             |
     catset1 |
          2  |   .0993448   .0385368     2.58   0.010     .0237467    .1749429
          3  |  -.0642978   .0552618    -1.16   0.245    -.1727056    .0441101
          4  |  -.0159738    .048064    -0.33   0.740    -.1102616     .078314
          5  |   .0034309    .042793     0.08   0.936    -.0805167    .0873785
          6  |  -.0330006    .046439    -0.71   0.477    -.1241005    .0580994
          7  |  -.0947912   .0397108    -2.39   0.017    -.1726923     -.01689
          8  |  -.1043221   .0386519    -2.70   0.007    -.1801461    -.028498
          9  |  -.1138453   .0629304    -1.81   0.071    -.2372968    .0096062
         10  |  -.0542691    .040375    -1.34   0.179    -.1334734    .0249352
         11  |  -.0666773   .0427333    -1.56   0.119    -.1505077    .0171531
         12  |   .0384355   .0392248     0.98   0.327    -.0385124    .1153834
         13  |   .2971356    .059641     4.98   0.000     .1801371    .4141341
         98  |  -.0804678   .0371966    -2.16   0.031    -.1534369   -.0074987
         99  |   .0110855   .0530672     0.21   0.835    -.0930172    .1151883
             |
        year |
       2011  |  -.0009915   .0346862    -0.03   0.977    -.0690359     .067053
       2012  |   .0262285   .0347445     0.75   0.450    -.0419302    .0943873
       2013  |   .0202012   .0346925     0.58   0.560    -.0478556     .088258
       2014  |   .0018523   .0349266     0.05   0.958    -.0666636    .0703682
       2015  |  -.0070303   .0353455    -0.20   0.842    -.0763681    .0623075
       2016  |  -.1045609   .0356668    -2.93   0.003     -.174529   -.0345927
       2017  |  -.0863454    .035781    -2.41   0.016    -.1565375   -.0161534
       2018  |   -.081293   .0365677    -2.22   0.026    -.1530284   -.0095576
       2019  |   -.112552   .0367253    -3.06   0.002    -.1845965   -.0405075
             |
       _cons |  -.2072043   .1252794    -1.65   0.098    -.4529665     .038558
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.53    0.654579
     votsom3 |     23.89    0.041860
     vots3_2 |     23.98    0.041694
      roe_ll |      1.14    0.875524
      tangib |      1.21    0.824310
   lnattotal |      1.50    0.664976
     catset1 |
          2  |      2.11    0.474168
          3  |      1.36    0.733471
          4  |      1.80    0.555497
          5  |      1.83    0.546069
          6  |      1.75    0.570478
          7  |      2.29    0.437201
          8  |      2.58    0.387812
          9  |      1.30    0.769386
         10  |      2.05    0.488606
         11  |      1.83    0.547596
         12  |      2.12    0.471189
         13  |      1.29    0.772641
         98  |      2.90    0.344944
         99  |      1.52    0.657885
        year |
       2011  |      1.88    0.532672
       2012  |      1.91    0.524250
       2013  |      1.95    0.513064
       2014  |      1.99    0.503173
       2015  |      2.04    0.491316
       2016  |      2.00    0.500613
       2017  |      1.97    0.507017
       2018  |      1.97    0.508524
       2019  |      1.90    0.525858
-------------+----------------------
    Mean VIF |      3.37

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom3

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1053.856     1    0.0000 |   1 |    1053.856     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots3_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1060.826     1    0.0000 |   1 |    1060.826     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   539.05
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub cgindex votsom3 vots3_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom3!
> =., re nolog vce (robust) 
. 
. regress dum_capsub cgindex votsom4 vots4_2 roe_ll tangib lnattotal i.catset1 i.year 
> if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom4!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.36
       Model |  24.2525596    29  .836295159           Prob > F      =  0.0000
    Residual |  109.525378  1357  .080711406           R-squared     =  0.1813
-------------+------------------------------           Adj R-squared =  0.1638
       Total |  133.777938  1386  .096520879           Root MSE      =   .2841

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.1155229   .0933646    -1.24   0.216    -.2986774    .0676316
     votsom4 |  -.2587167   .1821621    -1.42   0.156    -.6160666    .0986333
     vots4_2 |   .2193694   .1475683     1.49   0.137    -.0701174    .5088562
      roe_ll |  -.0494917   .0514513    -0.96   0.336    -.1504245    .0514411
      tangib |    .279975   .0501045     5.59   0.000     .1816842    .3782658
   lnattotal |   .0335906   .0064268     5.23   0.000     .0209832    .0461981
             |
     catset1 |
          2  |   .1005552   .0385409     2.61   0.009      .024949    .1761614
          3  |  -.0647572   .0553092    -1.17   0.242     -.173258    .0437437
          4  |  -.0142593    .048044    -0.30   0.767    -.1085078    .0799892
          5  |   .0044931   .0428272     0.10   0.916    -.0795217    .0885079
          6  |  -.0338363    .046535    -0.73   0.467    -.1251247    .0574521
          7  |  -.0941594   .0397041    -2.37   0.018    -.1720475   -.0162712
          8  |  -.1019573   .0387106    -2.63   0.009    -.1778964   -.0260182
          9  |  -.1118776   .0629114    -1.78   0.076    -.2352918    .0115365
         10  |  -.0546936   .0405024    -1.35   0.177    -.1341477    .0247606
         11  |  -.0657924   .0428006    -1.54   0.124    -.1497549      .01817
         12  |    .040266   .0391807     1.03   0.304    -.0365954    .1171273
         13  |   .2980597    .059637     5.00   0.000     .1810689    .4150504
         98  |  -.0791082   .0371584    -2.13   0.033    -.1520024    -.006214
         99  |   .0103196   .0530607     0.19   0.846    -.0937702    .1144095
             |
        year |
       2011  |  -.0011791   .0347029    -0.03   0.973    -.0692561     .066898
       2012  |   .0259558   .0347566     0.75   0.455    -.0422268    .0941384
       2013  |   .0203932   .0347197     0.59   0.557    -.0477169    .0885033
       2014  |   .0017663   .0349592     0.05   0.960    -.0668136    .0703462
       2015  |  -.0067989   .0353811    -0.19   0.848    -.0762065    .0626088
       2016  |  -.1047823   .0356988    -2.94   0.003    -.1748131   -.0347515
       2017  |  -.0863345    .035798    -2.41   0.016    -.1565599   -.0161091
       2018  |  -.0813055   .0365904    -2.22   0.026    -.1530854   -.0095255
       2019  |  -.1126137   .0367412    -3.07   0.002    -.1846894   -.0405379
             |
       _cons |  -.2061014   .1295008    -1.59   0.112    -.4601449    .0479421
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.54    0.648426
     votsom4 |     25.66    0.038970
     vots4_2 |     25.79    0.038768
      roe_ll |      1.14    0.875499
      tangib |      1.21    0.828172
   lnattotal |      1.51    0.663871
     catset1 |
          2  |      2.11    0.474332
          3  |      1.36    0.732623
          4  |      1.80    0.556272
          5  |      1.83    0.545501
          6  |      1.76    0.568443
          7  |      2.29    0.437592
          8  |      2.58    0.386854
          9  |      1.30    0.770283
         10  |      2.06    0.485810
         11  |      1.83    0.546181
         12  |      2.12    0.472515
         13  |      1.29    0.773177
         98  |      2.89    0.345847
         99  |      1.52    0.658417
        year |
       2011  |      1.88    0.532460
       2012  |      1.91    0.524177
       2013  |      1.95    0.512548
       2014  |      1.99    0.502516
       2015  |      2.04    0.490602
       2016  |      2.00    0.499997
       2017  |      1.97    0.506819
       2018  |      1.97    0.508177
       2019  |      1.90    0.525696
-------------+----------------------
    Mean VIF |      3.49

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom4

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.489     1    0.0000 |   1 |    1033.489     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots4_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1047.969     1    0.0000 |   1 |    1047.969     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   538.75
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub cgindex votsom4 vots4_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom4!
> =., re nolog vce (robust) 
. 
. regress dum_capsub cgindex votsom5 vots5_2 roe_ll tangib lnattotal i.catset1 i.year 
> if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom5!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.32
       Model |  24.1690907    29  .833416921           Prob > F      =  0.0000
    Residual |  109.608847  1357  .080772916           R-squared     =  0.1807
-------------+------------------------------           Adj R-squared =  0.1632
       Total |  133.777938  1386  .096520879           Root MSE      =  .28421

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.1168466   .0936287    -1.25   0.212    -.3005193    .0668261
     votsom5 |  -.1934973   .1893694    -1.02   0.307    -.5649858    .1779913
     vots5_2 |   .1636763   .1513509     1.08   0.280    -.1332308    .4605835
      roe_ll |     -.0498   .0514762    -0.97   0.333    -.1507817    .0511816
      tangib |   .2829122    .050052     5.65   0.000     .1847245       .3811
   lnattotal |   .0338476    .006427     5.27   0.000     .0212397    .0464555
             |
     catset1 |
          2  |   .1019936   .0385464     2.65   0.008     .0263766    .1776105
          3  |  -.0638306   .0553218    -1.15   0.249    -.1723561    .0446949
          4  |  -.0106295   .0479469    -0.22   0.825    -.1046875    .0834286
          5  |   .0072769   .0427808     0.17   0.865    -.0766468    .0912006
          6  |  -.0321372   .0465662    -0.69   0.490    -.1234868    .0592124
          7  |  -.0920947   .0396694    -2.32   0.020    -.1699147   -.0142748
          8  |  -.0989769   .0386992    -2.56   0.011    -.1748937   -.0230601
          9  |  -.1104597   .0629173    -1.76   0.079    -.2338854    .0129661
         10  |  -.0528612   .0405015    -1.31   0.192    -.1323136    .0265912
         11  |  -.0641344   .0428501    -1.50   0.135     -.148194    .0199253
         12  |   .0430441   .0391326     1.10   0.272    -.0337229     .119811
         13  |   .2994598    .059645     5.02   0.000     .1824535    .4164662
         98  |  -.0764189   .0370963    -2.06   0.040    -.1491913   -.0036465
         99  |   .0098948   .0530731     0.19   0.852    -.0942194    .1140089
             |
        year |
       2011  |   -.001631   .0347161    -0.05   0.963     -.069734    .0664721
       2012  |   .0256551   .0347696     0.74   0.461     -.042553    .0938631
       2013  |   .0199736   .0347418     0.57   0.565    -.0481797     .088127
       2014  |   .0016048   .0349913     0.05   0.963    -.0670381    .0702476
       2015  |  -.0071496   .0354216    -0.20   0.840    -.0766367    .0623375
       2016  |  -.1049409   .0357322    -2.94   0.003    -.1750372   -.0348447
       2017  |  -.0864356   .0358175    -2.41   0.016    -.1566992   -.0161719
       2018  |  -.0813654   .0366127    -2.22   0.026    -.1531891   -.0095418
       2019  |    -.11206   .0367534    -3.05   0.002    -.1841596   -.0399603
             |
       _cons |  -.2274989   .1317083    -1.73   0.084    -.4858729    .0308751
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.55    0.645264
     votsom5 |     26.11    0.038303
     vots5_2 |     26.26    0.038079
      roe_ll |      1.14    0.875319
      tangib |      1.20    0.830543
   lnattotal |      1.51    0.664335
     catset1 |
          2  |      2.11    0.474558
          3  |      1.36    0.732849
          4  |      1.79    0.558952
          5  |      1.83    0.547102
          6  |      1.76    0.568114
          7  |      2.28    0.438693
          8  |      2.58    0.387376
          9  |      1.30    0.770725
         10  |      2.06    0.486201
         11  |      1.83    0.545334
         12  |      2.11    0.474040
         13  |      1.29    0.773559
         98  |      2.88    0.347270
         99  |      1.52    0.658611
        year |
       2011  |      1.88    0.532459
       2012  |      1.91    0.524184
       2013  |      1.95    0.512288
       2014  |      1.99    0.501977
       2015  |      2.04    0.489854
       2016  |      2.00    0.499444
       2017  |      1.97    0.506652
       2018  |      1.97    0.507946
       2019  |      1.90    0.525748
-------------+----------------------
    Mean VIF |      3.52

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom5

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1015.172     1    0.0000 |   1 |    1015.172     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots5_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1036.555     1    0.0000 |   1 |    1036.555     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   538.87
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub cgindex votsom5 vots5_2 roe_ll tangib lnattotal i.catset1 i.yea
> r if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom5!
> =., re nolog vce (robust) 
. 
. ****************
. **EXVOT
. regress dum_capsub cgindex exvot1 roe_ll tangib lnattotal i.catset1 i.year if dum_ca
> psub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom2!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 28,  1358) =   10.70
       Model |  24.1851025    28  .863753662           Prob > F      =  0.0000
    Residual |  109.592835  1358  .080701646           R-squared     =  0.1808
-------------+------------------------------           Adj R-squared =  0.1639
       Total |  133.777938  1386  .096520879           Root MSE      =  .28408

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.0976162    .087601    -1.11   0.265    -.2694642    .0742317
      exvot1 |     .07761   .0660447     1.18   0.240    -.0519507    .2071706
      roe_ll |  -.0512422   .0514556    -1.00   0.319    -.1521832    .0496988
      tangib |   .2769007   .0506537     5.47   0.000     .1775327    .3762687
   lnattotal |   .0333256    .006467     5.15   0.000     .0206393    .0460119
             |
     catset1 |
          2  |    .096978   .0390274     2.48   0.013     .0204175    .1735386
          3  |  -.0655704    .054931    -1.19   0.233    -.1733292    .0421884
          4  |  -.0089468   .0476197    -0.19   0.851     -.102363    .0844695
          5  |   .0054623   .0429143     0.13   0.899    -.0787233    .0896479
          6  |  -.0293958   .0463455    -0.63   0.526    -.1203124    .0615207
          7  |  -.0906742   .0393861    -2.30   0.021    -.1679383   -.0134101
          8  |  -.1048298   .0392919    -2.67   0.008    -.1819091   -.0277504
          9  |  -.1169203   .0632147    -1.85   0.065    -.2409294    .0070887
         10  |  -.0500882   .0401661    -1.25   0.213    -.1288825    .0287061
         11  |  -.0710892   .0437465    -1.63   0.104    -.1569073    .0147288
         12  |   .0398986   .0394828     1.01   0.312    -.0375554    .1173526
         13  |   .2975831   .0597035     4.98   0.000      .180462    .4147043
         98  |  -.0746873   .0367659    -2.03   0.042    -.1468115    -.002563
         99  |    .006377   .0531618     0.12   0.905    -.0979112    .1106652
             |
        year |
       2011  |  -.0025116   .0346696    -0.07   0.942    -.0705234    .0655002
       2012  |   .0252254   .0347371     0.73   0.468    -.0429188    .0933696
       2013  |   .0185599   .0346693     0.54   0.593    -.0494513    .0865711
       2014  |   .0007998   .0348765     0.02   0.982    -.0676178    .0692173
       2015  |  -.0089452   .0352604    -0.25   0.800    -.0781161    .0602256
       2016  |  -.1060206   .0355733    -2.98   0.003    -.1758053    -.036236
       2017  |  -.0876749   .0357379    -2.45   0.014    -.1577823   -.0175675
       2018  |  -.0832562   .0365092    -2.28   0.023    -.1548767   -.0116357
       2019  |  -.1120266   .0367288    -3.05   0.002    -.1840778   -.0399753
             |
       _cons |  -.2834472   .1057971    -2.68   0.007    -.4909907   -.0759038
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.36    0.736468
      exvot1 |      1.52    0.658049
      roe_ll |      1.14    0.875250
      tangib |      1.23    0.810214
   lnattotal |      1.53    0.655565
     catset1 |
          2  |      2.16    0.462523
          3  |      1.35    0.742657
          4  |      1.77    0.566159
          5  |      1.84    0.543223
          6  |      1.75    0.573033
          7  |      2.25    0.444635
          8  |      2.66    0.375447
          9  |      1.31    0.762816
         10  |      2.02    0.493920
         11  |      1.91    0.522753
         12  |      2.15    0.465256
         13  |      1.30    0.771361
         98  |      2.83    0.353227
         99  |      1.52    0.655834
        year |
       2011  |      1.87    0.533417
       2012  |      1.91    0.524704
       2013  |      1.95    0.513977
       2014  |      1.98    0.504842
       2015  |      2.02    0.493906
       2016  |      1.99    0.503469
       2017  |      1.97    0.508464
       2018  |      1.96    0.510380
       2019  |      1.90    0.525989
-------------+----------------------
    Mean VIF |      1.83

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   537.28
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub cgindex exvot1 roe_ll tangib lnattotal i.catset1 i.year if dum_
> capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom2!=., re no
> log vce (robust)
. 
. regress dum_capsub cgindex exvot1 votsom1 roe_ll tangib lnattotal i.catset1 i.year i
> f dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom2!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.33
       Model |   24.196074    29  .834347378           Prob > F      =  0.0000
    Residual |  109.581864  1357  .080753032           R-squared     =  0.1809
-------------+------------------------------           Adj R-squared =  0.1634
       Total |  133.777938  1386  .096520879           Root MSE      =  .28417

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |  -.0884356   .0910998    -0.97   0.332    -.2671474    .0902762
      exvot1 |    .064879   .0745495     0.87   0.384    -.0813658    .2111237
     votsom1 |   .0161099    .043706     0.37   0.712    -.0696287    .1018485
      roe_ll |  -.0522007   .0515376    -1.01   0.311    -.1533027    .0489012
      tangib |    .277834    .050733     5.48   0.000     .1783103    .3773577
   lnattotal |   .0334916   .0064847     5.16   0.000     .0207705    .0462126
             |
     catset1 |
          2  |    .097199   .0390444     2.49   0.013     .0206049     .173793
          3  |  -.0629502   .0554064    -1.14   0.256    -.1716417    .0457414
          4  |  -.0072089   .0478677    -0.15   0.880    -.1011115    .0866938
          5  |   .0064577   .0430129     0.15   0.881    -.0779212    .0908366
          6  |  -.0270815   .0467835    -0.58   0.563    -.1188573    .0646943
          7  |  -.0893291   .0395673    -2.26   0.024    -.1669487   -.0117094
          8  |  -.1045827   .0393101    -2.66   0.008    -.1816978   -.0274675
          9  |  -.1171313   .0632374    -1.85   0.064     -.241185    .0069224
         10  |  -.0502996    .040183    -1.25   0.211    -.1291271    .0285278
         11  |  -.0701208   .0438392    -1.60   0.110    -.1561208    .0158792
         12  |   .0413664   .0396957     1.04   0.298    -.0365051     .119238
         13  |   .2962414   .0598333     4.95   0.000     .1788655    .4136173
         98  |  -.0731886   .0370017    -1.98   0.048    -.1457754   -.0006018
         99  |   .0076963   .0532991     0.14   0.885    -.0968612    .1122538
             |
        year |
       2011  |  -.0027376   .0346861    -0.08   0.937    -.0707817    .0653065
       2012  |   .0251414   .0347489     0.72   0.469    -.0430259    .0933088
       2013  |   .0183123   .0346868     0.53   0.598    -.0497334    .0863579
       2014  |    .000337   .0349101     0.01   0.992    -.0681467    .0688207
       2015  |  -.0096477   .0353231    -0.27   0.785    -.0789416    .0596461
       2016  |  -.1068129   .0356495    -3.00   0.003     -.176747   -.0368787
       2017  |  -.0883625   .0357979    -2.47   0.014    -.1585877   -.0181373
       2018  |  -.0839928   .0365754    -2.30   0.022    -.1557434   -.0122423
       2019  |  -.1123546   .0367512    -3.06   0.002    -.1844501   -.0402592
             |
       _cons |  -.2977351   .1127063    -2.64   0.008    -.5188326   -.0766376
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.47    0.681418
      exvot1 |      1.93    0.516798
     votsom1 |      1.75    0.571452
      roe_ll |      1.15    0.873021
      tangib |      1.24    0.808196
   lnattotal |      1.53    0.652405
     catset1 |
          2  |      2.16    0.462414
          3  |      1.37    0.730432
          4  |      1.78    0.560666
          5  |      1.85    0.541082
          6  |      1.78    0.562711
          7  |      2.27    0.440852
          8  |      2.66    0.375338
          9  |      1.31    0.762754
         10  |      2.03    0.493819
         11  |      1.92    0.520875
         12  |      2.17    0.460573
         13  |      1.30    0.768507
         98  |      2.87    0.348962
         99  |      1.53    0.652877
        year |
       2011  |      1.88    0.533250
       2012  |      1.91    0.524681
       2013  |      1.95    0.513785
       2014  |      1.98    0.504189
       2015  |      2.03    0.492468
       2016  |      1.99    0.501639
       2017  |      1.97    0.507083
       2018  |      1.97    0.508856
       2019  |      1.90    0.525681
-------------+----------------------
    Mean VIF |      1.85

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   537.48
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub cgindex exvot1 votsom1 roe_ll tangib lnattotal i.catset1 i.year
>  if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom2!=
> ., re nolog vce (robust)
. 
. regress dum_capsub cgindex exvot1 votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 
> i.year if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & vot
> som2!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 30,  1356) =   10.01
       Model |  24.2474884    30  .808249612           Prob > F      =  0.0000
    Residual |   109.53045  1356  .080774668           R-squared     =  0.1813
-------------+------------------------------           Adj R-squared =  0.1631
       Total |  133.777938  1386  .096520879           Root MSE      =  .28421

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cgindex |   -.096663   .0916938    -1.05   0.292    -.2765401     .083214
      exvot1 |   .0449605   .0786284     0.57   0.568     -.109286     .199207
     votsom1 |   -.083266   .1320066    -0.63   0.528    -.3422254    .1756934
     vots1_2 |   .1097974   .1376218     0.80   0.425    -.1601773    .3797721
      roe_ll |   -.051683   .0515486    -1.00   0.316    -.1528066    .0494406
      tangib |    .274569   .0509046     5.39   0.000     .1747086    .3744293
   lnattotal |   .0340139   .0065185     5.22   0.000     .0212264    .0468013
             |
     catset1 |
          2  |    .097539    .039052     2.50   0.013     .0209301    .1741479
          3  |  -.0639227   .0554272    -1.15   0.249    -.1726551    .0448098
          4  |  -.0111416   .0481272    -0.23   0.817    -.1055535    .0832702
          5  |   .0065336   .0430187     0.15   0.879    -.0778569     .090924
          6  |  -.0281671   .0468095    -0.60   0.547    -.1199941    .0636598
          7  |  -.0903767   .0395943    -2.28   0.023    -.1680495   -.0127039
          8  |  -.1056459   .0393379    -2.69   0.007    -.1828157    -.028476
          9  |  -.1150609   .0632991    -1.82   0.069    -.2392357    .0091139
         10  |  -.0503879   .0401885    -1.25   0.210    -.1292263    .0284505
         11  |  -.0731681   .0440112    -1.66   0.097    -.1595055    .0131692
         12  |   .0405567   .0397139     1.02   0.307    -.0373508    .1184641
         13  |   .2984614    .059906     4.98   0.000     .1809428    .4159799
         98  |  -.0756094   .0371309    -2.04   0.042    -.1484496   -.0027693
         99  |   .0092196   .0533404     0.17   0.863     -.095419    .1138582
             |
        year |
       2011  |  -.0023053   .0346949    -0.07   0.947    -.0703668    .0657563
       2012  |   .0258392   .0347646     0.74   0.457    -.0423589    .0940374
       2013  |   .0189031   .0346994     0.54   0.586    -.0491672    .0869734
       2014  |   .0006105   .0349165     0.02   0.986    -.0678857    .0691067
       2015  |  -.0092587   .0353312    -0.26   0.793    -.0785685     .060051
       2016  |  -.1067932   .0356543    -3.00   0.003    -.1767367   -.0368496
       2017  |  -.0882148   .0358031    -2.46   0.014    -.1584504   -.0179793
       2018  |  -.0834162   .0365875    -2.28   0.023    -.1551904    -.011642
       2019  |  -.1124044   .0367562    -3.06   0.002    -.1845097   -.0402992
             |
       _cons |  -.2813575   .1145753    -2.46   0.014    -.5061217   -.0565934
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
     cgindex |      1.49    0.672799
      exvot1 |      2.15    0.464694
     votsom1 |     15.96    0.062659
     vots1_2 |     16.78    0.059593
      roe_ll |      1.15    0.872883
      tangib |      1.25    0.802973
   lnattotal |      1.55    0.645824
     catset1 |
          2  |      2.16    0.462359
          3  |      1.37    0.730079
          4  |      1.80    0.554784
          5  |      1.85    0.541079
          6  |      1.78    0.562236
          7  |      2.27    0.440367
          8  |      2.67    0.374907
          9  |      1.31    0.761472
         10  |      2.03    0.493815
         11  |      1.93    0.516952
         12  |      2.17    0.460273
         13  |      1.30    0.766849
         98  |      2.88    0.346632
         99  |      1.53    0.652040
        year |
       2011  |      1.88    0.533120
       2012  |      1.91    0.524349
       2013  |      1.95    0.513551
       2014  |      1.98    0.504140
       2015  |      2.03    0.492375
       2016  |      1.99    0.501638
       2017  |      1.97    0.507070
       2018  |      1.97    0.508658
       2019  |      1.90    0.525679
-------------+----------------------
    Mean VIF |      2.83

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots1_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.033     1    0.0000 |   1 |    1033.033     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   538.17
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub cgindex exvot1 votsom1 vots1_2 roe_ll tangib lnattotal i.catset
> 1 i.year if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & v
> otsom2!=., re nolog vce (robust)
. 
. ************************************************************************************
> ***
. ** USANDO DUMMY NM
. ************************************************************************************
> ***
. regress dum_capsub d_nm votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.year if 
> dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.45
       Model |  24.4157746    29  .841923261           Prob > F      =  0.0000
    Residual |  109.362163  1357   .08059113           R-squared     =  0.1825
-------------+------------------------------           Adj R-squared =  0.1650
       Total |  133.777938  1386  .096520879           Root MSE      =  .28389

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0415078   .0214895    -1.93   0.054     -.083664    .0006483
     votsom1 |  -.0953192   .1289551    -0.74   0.460    -.3482922    .1576538
     vots1_2 |   .1221655   .1300121     0.94   0.348    -.1328811     .377212
      roe_ll |   -.056898   .0514972    -1.10   0.269    -.1579207    .0441247
      tangib |    .270671    .050563     5.35   0.000      .171481    .3698611
   lnattotal |   .0306834   .0065046     4.72   0.000     .0179232    .0434435
             |
     catset1 |
          2  |   .0911544   .0387363     2.35   0.019      .015165    .1671439
          3  |  -.0663342   .0552653    -1.20   0.230    -.1747489    .0420804
          4  |  -.0167749    .047879    -0.35   0.726    -.1106998    .0771499
          5  |  -.0083296   .0432864    -0.19   0.847    -.0932452     .076586
          6  |  -.0333846   .0468038    -0.71   0.476    -.1252002     .058431
          7  |  -.0910155   .0394467    -2.31   0.021    -.1683985   -.0136325
          8  |  -.1158437    .038881    -2.98   0.003    -.1921172   -.0395702
          9  |  -.1231628   .0631892    -1.95   0.051     -.247122    .0007964
         10  |  -.0585819   .0400986    -1.46   0.144    -.1372438      .02008
         11  |  -.0926394   .0454148    -2.04   0.042    -.1817302   -.0035486
         12  |   .0280139   .0397971     0.70   0.482    -.0500567    .1060844
         13  |   .2963085   .0597785     4.96   0.000     .1790403    .4135767
         98  |  -.0825144     .03705    -2.23   0.026    -.1551958    -.009833
         99  |     .00285   .0533667     0.05   0.957    -.1018402    .1075401
             |
        year |
       2011  |  -.0027826   .0346325    -0.08   0.936    -.0707217    .0651564
       2012  |   .0237052   .0346003     0.69   0.493    -.0441706     .091581
       2013  |   .0149315   .0343679     0.43   0.664    -.0524885    .0823516
       2014  |  -.0046922   .0344404    -0.14   0.892    -.0722544    .0628699
       2015  |  -.0158986   .0347444    -0.46   0.647    -.0840572    .0522601
       2016  |  -.1133459   .0349953    -3.24   0.001    -.1819968   -.0446951
       2017  |  -.0949559   .0350594    -2.71   0.007    -.1637323   -.0261794
       2018  |  -.0919739   .0354047    -2.60   0.009    -.1614277     -.02252
       2019  |  -.1203195   .0358249    -3.36   0.001    -.1905976   -.0500413
             |
       _cons |  -.2455091    .115545    -2.12   0.034    -.4721754   -.0188429
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      1.73    0.578812
     votsom1 |     15.26    0.065511
     vots1_2 |     15.01    0.066621
      roe_ll |      1.15    0.872639
      tangib |      1.23    0.812012
   lnattotal |      1.55    0.647116
     catset1 |
          2  |      2.13    0.468859
          3  |      1.36    0.732696
          4  |      1.79    0.559277
          5  |      1.88    0.533193
          6  |      1.78    0.561096
          7  |      2.26    0.442663
          8  |      2.61    0.382898
          9  |      1.31    0.762386
         10  |      2.02    0.494906
         11  |      2.06    0.484388
         12  |      2.19    0.457309
         13  |      1.30    0.768375
         98  |      2.88    0.347356
         99  |      1.54    0.649918
        year |
       2011  |      1.87    0.533828
       2012  |      1.89    0.528138
       2013  |      1.91    0.522315
       2014  |      1.93    0.516998
       2015  |      1.97    0.507989
       2016  |      1.92    0.519524
       2017  |      1.90    0.527611
       2018  |      1.85    0.541978
       2019  |      1.81    0.552109
-------------+----------------------
    Mean VIF |      2.76

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots1_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.033     1    0.0000 |   1 |    1033.033     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   542.22
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub d_nm votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.year i
> f dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=.,
>  re nolog vce (robust)
. 
. regress dum_capsub  d_nm votsom2 vots2_2 roe_ll tangib lnattotal i.catset1 i.year if
>  dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom2!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.50
       Model |  24.5170273    29  .845414735           Prob > F      =  0.0000
    Residual |  109.260911  1357  .080516515           R-squared     =  0.1833
-------------+------------------------------           Adj R-squared =  0.1658
       Total |  133.777938  1386  .096520879           Root MSE      =  .28375

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0427134   .0227157    -1.88   0.060    -.0872751    .0018483
     votsom2 |  -.2310149   .1503611    -1.54   0.125    -.5259803    .0639506
     vots2_2 |   .2077156   .1350816     1.54   0.124    -.0572758     .472707
      roe_ll |  -.0552881   .0514543    -1.07   0.283    -.1562266    .0456504
      tangib |   .2666082   .0505694     5.27   0.000     .1674055    .3658109
   lnattotal |    .029847   .0065023     4.59   0.000     .0170913    .0426027
             |
     catset1 |
          2  |   .0892516   .0387828     2.30   0.022     .0131708    .1653324
          3  |  -.0708428    .055373    -1.28   0.201    -.1794687    .0377831
          4  |   -.024031   .0478788    -0.50   0.616    -.1179555    .0698936
          5  |  -.0138534     .04339    -0.32   0.750    -.0989722    .0712654
          6  |  -.0387258   .0466381    -0.83   0.406    -.1302165    .0527649
          7  |  -.0969107     .03966    -2.44   0.015    -.1747122   -.0191092
          8  |   -.120521   .0393164    -3.07   0.002    -.1976485   -.0433935
          9  |  -.1234026   .0631801    -1.95   0.051    -.2473438    .0005387
         10  |  -.0613723   .0401133    -1.53   0.126    -.1400632    .0173186
         11  |  -.0926943   .0450736    -2.06   0.040    -.1811158   -.0042728
         12  |   .0220974    .039982     0.55   0.581    -.0563359    .1005307
         13  |   .2922368   .0597442     4.89   0.000     .1750357    .4094379
         98  |  -.0894736   .0372736    -2.40   0.017    -.1625938   -.0163533
         99  |   .0020462    .053413     0.04   0.969    -.1027348    .1068271
             |
        year |
       2011  |  -.0019807   .0346207    -0.06   0.954    -.0698966    .0659353
       2012  |   .0234863   .0345785     0.68   0.497    -.0443468    .0913194
       2013  |   .0148951   .0343515     0.43   0.665    -.0524928     .082283
       2014  |   -.004577   .0344235    -0.13   0.894    -.0721061    .0629521
       2015  |  -.0154254   .0347293    -0.44   0.657    -.0835542    .0527035
       2016  |  -.1125217   .0349746    -3.22   0.001    -.1811319   -.0439114
       2017  |   -.094461   .0350431    -2.70   0.007    -.1632055   -.0257164
       2018  |  -.0917547   .0353897    -2.59   0.010    -.1611791   -.0223302
       2019  |   -.121598   .0358271    -3.39   0.001    -.1918804   -.0513156
             |
       _cons |  -.1872783   .1211233    -1.55   0.122    -.4248875    .0503308
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      1.93    0.517528
     votsom2 |     20.84    0.047981
     vots2_2 |     21.59    0.046325
      roe_ll |      1.15    0.873286
      tangib |      1.23    0.811053
   lnattotal |      1.55    0.646967
     catset1 |
          2  |      2.14    0.467301
          3  |      1.37    0.729173
          4  |      1.79    0.558763
          5  |      1.89    0.530159
          6  |      1.77    0.564566
          7  |      2.29    0.437508
          8  |      2.67    0.374119
          9  |      1.31    0.761900
         10  |      2.02    0.494083
         11  |      2.04    0.491294
         12  |      2.21    0.452670
         13  |      1.30    0.768543
         98  |      2.92    0.342882
         99  |      1.54    0.648191
        year |
       2011  |      1.87    0.533697
       2012  |      1.89    0.528313
       2013  |      1.91    0.522329
       2014  |      1.93    0.517026
       2015  |      1.97    0.507962
       2016  |      1.92    0.519658
       2017  |      1.90    0.527612
       2018  |      1.85    0.541934
       2019  |      1.81    0.551531
-------------+----------------------
    Mean VIF |      3.19

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1073.819     1    0.0000 |   1 |    1073.819     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots2_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1073.196     1    0.0000 |   1 |    1073.196     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   540.74
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub  d_nm votsom2 vots2_2 roe_ll tangib lnattotal i.catset1 i.year 
> if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom2!=.
> , re nolog vce (robust)
. 
. regress dum_capsub d_nm votsom3 vots3_2 roe_ll tangib lnattotal i.catset1 i.year if 
> dum_capsub !=. & d_nm !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom3!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.48
       Model |  24.4800484    29  .844139602           Prob > F      =  0.0000
    Residual |   109.29789  1357  .080543765           R-squared     =  0.1830
-------------+------------------------------           Adj R-squared =  0.1655
       Total |  133.777938  1386  .096520879           Root MSE      =   .2838

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0440053   .0233992    -1.88   0.060    -.0899079    .0018973
     votsom3 |    -.23554   .1687812    -1.40   0.163    -.5666404    .0955604
     vots3_2 |   .1913889   .1438765     1.33   0.184    -.0908556    .4736334
      roe_ll |  -.0558455   .0514804    -1.08   0.278    -.1568354    .0451444
      tangib |   .2699995   .0503777     5.36   0.000     .1711728    .3688262
   lnattotal |   .0294422   .0065292     4.51   0.000     .0166338    .0422505
             |
     catset1 |
          2  |   .0901703   .0387639     2.33   0.020     .0141266    .1662139
          3  |   -.070739    .055391    -1.28   0.202    -.1794004    .0379223
          4  |  -.0235858   .0479263    -0.49   0.623    -.1176035    .0704318
          5  |  -.0147347    .043487    -0.34   0.735    -.1000438    .0705744
          6  |  -.0383308   .0465489    -0.82   0.410    -.1296464    .0529849
          7  |  -.0971613   .0397134    -2.45   0.015    -.1750677    -.019255
          8  |  -.1172705   .0391059    -3.00   0.003    -.1939851   -.0405559
          9  |  -.1241804   .0632173    -1.96   0.050    -.2481947   -.0001661
         10  |  -.0619449   .0401687    -1.54   0.123    -.1407444    .0168545
         11  |  -.0908312   .0450533    -2.02   0.044    -.1792128   -.0024496
         12  |   .0217011   .0400761     0.54   0.588    -.0569167    .1003189
         13  |   .2938323   .0596326     4.93   0.000     .1768502    .4108144
         98  |  -.0893854   .0372889    -2.40   0.017    -.1625356   -.0162353
         99  |   .0013122   .0534481     0.02   0.980    -.1035376     .106162
             |
        year |
       2011  |  -.0016848   .0346339    -0.05   0.961    -.0696266     .066257
       2012  |   .0235342   .0345848     0.68   0.496    -.0443113    .0913797
       2013  |   .0152843   .0343604     0.44   0.657     -.052121    .0826897
       2014  |  -.0042247   .0344304    -0.12   0.902    -.0717672    .0633178
       2015  |  -.0146977   .0347431    -0.42   0.672    -.0828538    .0534584
       2016  |  -.1120401   .0349832    -3.20   0.001    -.1806671   -.0434132
       2017  |  -.0943189   .0350531    -2.69   0.007     -.163083   -.0255547
       2018  |   -.091684   .0353998    -2.59   0.010    -.1611282   -.0222398
       2019  |  -.1221858   .0358648    -3.41   0.001    -.1925423   -.0518293
             |
       _cons |   -.171688   .1261924    -1.36   0.174    -.4192413    .0758653
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.05    0.487899
     votsom3 |     23.95    0.041752
     vots3_2 |     25.10    0.039834
      roe_ll |      1.15    0.872693
      tangib |      1.22    0.817513
   lnattotal |      1.56    0.641877
     catset1 |
          2  |      2.14    0.467916
          3  |      1.37    0.728944
          4  |      1.79    0.557846
          5  |      1.89    0.527975
          6  |      1.76    0.566923
          7  |      2.29    0.436479
          8  |      2.64    0.378285
          9  |      1.31    0.761261
         10  |      2.03    0.492890
         11  |      2.03    0.491904
         12  |      2.22    0.450700
         13  |      1.30    0.771684
         98  |      2.92    0.342718
         99  |      1.54    0.647559
        year |
       2011  |      1.87    0.533471
       2012  |      1.89    0.528299
       2013  |      1.91    0.522236
       2014  |      1.93    0.516995
       2015  |      1.97    0.507728
       2016  |      1.92    0.519581
       2017  |      1.90    0.527490
       2018  |      1.85    0.541810
       2019  |      1.82    0.550557
-------------+----------------------
    Mean VIF |      3.43

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom3

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1053.856     1    0.0000 |   1 |    1053.856     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots3_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1060.826     1    0.0000 |   1 |    1060.826     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   542.97
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub d_nm votsom3 vots3_2 roe_ll tangib lnattotal i.catset1 i.year i
> f dum_capsub !=. & d_nm !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom3!=., re
>  nolog vce (robust) 
. 
. regress dum_capsub d_nm votsom4 vots4_2 roe_ll tangib lnattotal i.catset1 i.year if 
> dum_capsub !=. & d_nm !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom4!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.45
       Model |  24.4292035    29  .842386328           Prob > F      =  0.0000
    Residual |  109.348734  1357  .080581234           R-squared     =  0.1826
-------------+------------------------------           Adj R-squared =  0.1651
       Total |  133.777938  1386  .096520879           Root MSE      =  .28387

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0458857   .0237728    -1.93   0.054     -.092521    .0007497
     votsom4 |  -.2055062   .1829853    -1.12   0.262    -.5644709    .1534585
     vots4_2 |    .155653   .1520856     1.02   0.306    -.1426954    .4540014
      roe_ll |  -.0566243   .0515325    -1.10   0.272    -.1577163    .0444677
      tangib |   .2728508   .0502743     5.43   0.000     .1742271    .3714745
   lnattotal |   .0292848   .0065542     4.47   0.000     .0164273    .0421422
             |
     catset1 |
          2  |   .0912283   .0387568     2.35   0.019     .0151985    .1672581
          3  |  -.0715148   .0554436    -1.29   0.197    -.1802792    .0372496
          4  |  -.0219677   .0479053    -0.46   0.647    -.1159443    .0720088
          5  |  -.0139077   .0434676    -0.32   0.749    -.0991787    .0713633
          6  |  -.0379191   .0465762    -0.81   0.416    -.1292882    .0534501
          7  |  -.0962609   .0396965    -2.42   0.015    -.1741341   -.0183877
          8  |  -.1147736   .0390928    -2.94   0.003    -.1914624   -.0380848
          9  |  -.1226147   .0632013    -1.94   0.053    -.2465975    .0013682
         10  |  -.0618668   .0402559    -1.54   0.125    -.1408373    .0171037
         11  |  -.0903554    .045031    -2.01   0.045    -.1786933   -.0020175
         12  |   .0231527   .0400054     0.58   0.563    -.0553266    .1016319
         13  |    .294528   .0596311     4.94   0.000     .1775488    .4115071
         98  |  -.0880439    .037227    -2.37   0.018    -.1610725   -.0150152
         99  |   .0006054   .0534073     0.01   0.991    -.1041645    .1053753
             |
        year |
       2011  |  -.0018004   .0346464    -0.05   0.959    -.0697667    .0661659
       2012  |   .0232821   .0345912     0.67   0.501    -.0445759    .0911401
       2013  |   .0153545   .0343719     0.45   0.655    -.0520734    .0827825
       2014  |  -.0043028   .0344389    -0.12   0.901    -.0718621    .0632565
       2015  |  -.0146547   .0347566    -0.42   0.673    -.0828372    .0535278
       2016  |  -.1122733   .0349927    -3.21   0.001    -.1809189   -.0436276
       2017  |  -.0945105   .0350601    -2.70   0.007    -.1632885   -.0257326
       2018  |  -.0919306   .0354059    -2.60   0.010    -.1613868   -.0224744
       2019  |  -.1225132   .0358944    -3.41   0.001    -.1929278   -.0520986
             |
       _cons |  -.1716825   .1297541    -1.32   0.186    -.4262229     .082858
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.11    0.472906
     votsom4 |     25.94    0.038557
     vots4_2 |     27.44    0.036440
      roe_ll |      1.15    0.871336
      tangib |      1.22    0.821263
   lnattotal |      1.57    0.637283
     catset1 |
          2  |      2.14    0.468305
          3  |      1.37    0.727902
          4  |      1.79    0.558593
          5  |      1.89    0.528692
          6  |      1.77    0.566524
          7  |      2.29    0.437054
          8  |      2.64    0.378715
          9  |      1.31    0.762002
         10  |      2.04    0.490985
         11  |      2.03    0.492620
         12  |      2.21    0.452503
         13  |      1.30    0.772082
         98  |      2.91    0.344019
         99  |      1.54    0.648849
        year |
       2011  |      1.87    0.533335
       2012  |      1.89    0.528350
       2013  |      1.92    0.522129
       2014  |      1.93    0.516979
       2015  |      1.97    0.507571
       2016  |      1.92    0.519540
       2017  |      1.90    0.527523
       2018  |      1.85    0.541875
       2019  |      1.82    0.549904
-------------+----------------------
    Mean VIF |      3.58

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom4

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.489     1    0.0000 |   1 |    1033.489     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots4_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1047.969     1    0.0000 |   1 |    1047.969     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   543.60
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub d_nm votsom4 vots4_2 roe_ll tangib lnattotal i.catset1 i.year i
> f dum_capsub !=. & d_nm !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom4!=., re
>  nolog vce (robust) 
. 
. regress dum_capsub d_nm votsom5 vots5_2 roe_ll tangib lnattotal i.catset1 i.year if 
> dum_capsub !=. & d_nm !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom5!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.42
       Model |  24.3668974    29   .84023784           Prob > F      =  0.0000
    Residual |  109.411041  1357  .080627149           R-squared     =  0.1821
-------------+------------------------------           Adj R-squared =  0.1647
       Total |  133.777938  1386  .096520879           Root MSE      =  .28395

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0479729   .0239457    -2.00   0.045    -.0949476   -.0009982
     votsom5 |  -.1260368   .1907502    -0.66   0.509     -.500234    .2481604
     vots5_2 |   .0872026   .1569784     0.56   0.579     -.220744    .3951491
      roe_ll |  -.0573811   .0515672    -1.11   0.266    -.1585412     .043779
      tangib |   .2753748   .0502184     5.48   0.000     .1768606     .373889
   lnattotal |   .0293592   .0065709     4.47   0.000     .0164689    .0422495
             |
     catset1 |
          2  |   .0923907   .0387587     2.38   0.017     .0163572    .1684241
          3  |  -.0709029   .0554496    -1.28   0.201    -.1796792    .0378733
          4  |  -.0185707     .04781    -0.39   0.698    -.1123601    .0752188
          5  |  -.0117818   .0434224    -0.27   0.786     -.096964    .0734005
          6  |   -.036205   .0465912    -0.78   0.437    -.1276036    .0551936
          7  |  -.0939788   .0396514    -2.37   0.018    -.1717635   -.0161942
          8  |  -.1120333    .039066    -2.87   0.004    -.1886697   -.0353969
          9  |  -.1217584   .0632067    -1.93   0.054    -.2457519    .0022351
         10  |  -.0600578   .0402514    -1.49   0.136    -.1390194    .0189039
         11  |  -.0894838   .0450197    -1.99   0.047    -.1777996    -.001168
         12  |   .0254585   .0399264     0.64   0.524    -.0528656    .1037826
         13  |   .2956076   .0596386     4.96   0.000     .1786138    .4126015
         98  |  -.0853663   .0371407    -2.30   0.022    -.1582257   -.0125069
         99  |   .0002108   .0533727     0.00   0.997    -.1044912    .1049129
             |
        year |
       2011  |  -.0022153   .0346554    -0.06   0.949    -.0701992    .0657686
       2012  |   .0230434      .0346     0.67   0.506    -.0448318    .0909187
       2013  |   .0149649   .0343803     0.44   0.663    -.0524795    .0824092
       2014  |  -.0043774   .0344511    -0.13   0.899    -.0719605    .0632058
       2015  |  -.0149915   .0347695    -0.43   0.666    -.0831992    .0532163
       2016  |  -.1123373   .0350061    -3.21   0.001    -.1810092   -.0436653
       2017  |  -.0946767   .0350692    -2.70   0.007    -.1634725   -.0258809
       2018  |  -.0920723   .0354149    -2.60   0.009    -.1615462   -.0225984
       2019  |  -.1220773   .0359146    -3.40   0.001    -.1925314   -.0516231
             |
       _cons |  -.1925981   .1312797    -1.47   0.143    -.4501314    .0649352
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.14    0.466365
     votsom5 |     26.54    0.037682
     vots5_2 |     28.30    0.035334
      roe_ll |      1.15    0.870659
      tangib |      1.21    0.823559
   lnattotal |      1.58    0.634398
     catset1 |
          2  |      2.13    0.468526
          3  |      1.37    0.728157
          4  |      1.78    0.561144
          5  |      1.89    0.530097
          6  |      1.77    0.566482
          7  |      2.28    0.438300
          8  |      2.64    0.379450
          9  |      1.31    0.762304
         10  |      2.04    0.491375
         11  |      2.03    0.493147
         12  |      2.20    0.454557
         13  |      1.29    0.772328
         98  |      2.89    0.345816
         99  |      1.54    0.650060
        year |
       2011  |      1.87    0.533362
       2012  |      1.89    0.528383
       2013  |      1.92    0.522172
       2014  |      1.93    0.516908
       2015  |      1.97    0.507484
       2016  |      1.93    0.519437
       2017  |      1.90    0.527550
       2018  |      1.85    0.541907
       2019  |      1.82    0.549600
-------------+----------------------
    Mean VIF |      3.63

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom5

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1015.172     1    0.0000 |   1 |    1015.172     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots5_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1036.555     1    0.0000 |   1 |    1036.555     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   544.76
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub d_nm votsom5 vots5_2 roe_ll tangib lnattotal i.catset1 i.year i
> f dum_capsub !=. & d_nm !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom5!=., re
>  nolog vce (robust) 
. 
. ****************
. **EXVOT
. regress dum_capsub d_nm exvot1 roe_ll tangib lnattotal i.catset1 i.year if dum_capsu
> b !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 28,  1358) =   10.78
       Model |  24.3274148    28  .868836242           Prob > F      =  0.0000
    Residual |  109.450523  1358  .080596851           R-squared     =  0.1818
-------------+------------------------------           Adj R-squared =  0.1650
       Total |  133.777938  1386  .096520879           Root MSE      =   .2839

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0425209   .0245124    -1.73   0.083    -.0906073    .0055654
      exvot1 |    .018484   .0782617     0.24   0.813    -.1350429    .1720109
      roe_ll |   -.056476   .0514777    -1.10   0.273    -.1574606    .0445085
      tangib |   .2754606    .050632     5.44   0.000     .1761351    .3747861
   lnattotal |   .0300308   .0064399     4.66   0.000     .0173975    .0426641
             |
     catset1 |
          2  |    .092229   .0390399     2.36   0.018     .0156439    .1688141
          3  |  -.0672949   .0548895    -1.23   0.220    -.1749724    .0403825
          4  |  -.0135564   .0472429    -0.29   0.774    -.1062334    .0791206
          5  |  -.0083857    .043108    -0.19   0.846    -.0929511    .0761797
          6  |  -.0339033   .0464386    -0.73   0.465    -.1250024    .0571958
          7  |  -.0906058   .0393527    -2.30   0.021    -.1678044   -.0134072
          8  |  -.1122456   .0394193    -2.85   0.004     -.189575   -.0349163
          9  |  -.1233179    .063344    -1.95   0.052    -.2475805    .0009447
         10  |  -.0581747   .0400909    -1.45   0.147    -.1368215    .0204721
         11  |  -.0881206   .0449887    -1.96   0.050    -.1763755    .0001342
         12  |   .0288842   .0395901     0.73   0.466    -.0487803    .1065487
         13  |   .2961823   .0596739     4.96   0.000     .1791193    .4132453
         98  |  -.0808004   .0365481    -2.21   0.027    -.1524973   -.0091035
         99  |   .0006481   .0532959     0.01   0.990    -.1039032    .1051993
             |
        year |
       2011  |   -.003072   .0346292    -0.09   0.929    -.0710046    .0648605
       2012  |   .0229304   .0345931     0.66   0.508    -.0449313    .0907922
       2013  |   .0144374   .0343643     0.42   0.674    -.0529755    .0818502
       2014  |  -.0046627   .0344415    -0.14   0.892    -.0722271    .0629016
       2015  |  -.0157172   .0347475    -0.45   0.651    -.0838818    .0524473
       2016  |  -.1126313   .0349906    -3.22   0.001    -.1812729   -.0439897
       2017  |  -.0945846   .0350602    -2.70   0.007    -.1633626   -.0258066
       2018  |  -.0919626   .0354029    -2.60   0.009    -.1614128   -.0225123
       2019  |  -.1201094   .0357913    -3.36   0.001    -.1903216   -.0498972
             |
       _cons |   -.250596   .1083391    -2.31   0.021    -.4631261   -.0380658
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.25    0.444883
      exvot1 |      2.14    0.468027
      roe_ll |      1.15    0.873360
      tangib |      1.23    0.809855
   lnattotal |      1.51    0.660219
     catset1 |
          2  |      2.17    0.461627
          3  |      1.35    0.742814
          4  |      1.74    0.574479
          5  |      1.86    0.537655
          6  |      1.75    0.569997
          7  |      2.25    0.444812
          8  |      2.68    0.372539
          9  |      1.32    0.758720
         10  |      2.02    0.495130
         11  |      2.03    0.493642
         12  |      2.16    0.462136
         13  |      1.30    0.771125
         98  |      2.80    0.356986
         99  |      1.53    0.651691
        year |
       2011  |      1.87    0.533967
       2012  |      1.89    0.528393
       2013  |      1.91    0.522463
       2014  |      1.93    0.517000
       2015  |      1.97    0.507936
       2016  |      1.92    0.519701
       2017  |      1.90    0.527624
       2018  |      1.84    0.542072
       2019  |      1.81    0.553185
-------------+----------------------
    Mean VIF |      1.87

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   543.43
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub d_nm exvot1 roe_ll tangib lnattotal i.catset1 i.year if dum_cap
> sub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=., re nolog
>  vce (robust)
. 
. regress dum_capsub d_nm exvot1 votsom1 roe_ll tangib lnattotal i.catset1 i.year if d
> um_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 29,  1357) =   10.41
       Model |  24.3447262    29  .839473316           Prob > F      =  0.0000
    Residual |  109.433212  1357  .080643487           R-squared     =  0.1820
-------------+------------------------------           Adj R-squared =  0.1645
       Total |  133.777938  1386  .096520879           Root MSE      =  .28398

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0412065   .0246831    -1.67   0.095    -.0896277    .0072147
      exvot1 |   .0031169   .0850206     0.04   0.971    -.1636693    .1699031
     votsom1 |    .019595   .0422925     0.46   0.643    -.0633708    .1025607
      roe_ll |  -.0574075   .0515319    -1.11   0.265    -.1584983    .0436832
      tangib |   .2765791   .0507042     5.45   0.000      .177112    .3760462
   lnattotal |   .0304492   .0065048     4.68   0.000     .0176887    .0432098
             |
     catset1 |
          2  |   .0927461   .0390672     2.37   0.018     .0161075    .1693847
          3  |  -.0639707   .0553722    -1.16   0.248    -.1725951    .0446536
          4  |  -.0107573   .0476412    -0.23   0.821    -.1042157    .0827011
          5  |   -.006411   .0433306    -0.15   0.882    -.0914132    .0785912
          6  |  -.0312484   .0468041    -0.67   0.504    -.1230646    .0605679
          7  |  -.0888721   .0395415    -2.25   0.025    -.1664412   -.0113031
          8  |  -.1116206   .0394538    -2.83   0.005    -.1890177   -.0342235
          9  |  -.1235976   .0633652    -1.95   0.051    -.2479019    .0007067
         10  |  -.0578793   .0401076    -1.44   0.149    -.1365588    .0208002
         11  |  -.0865448   .0451301    -1.92   0.055     -.175077    .0019875
         12  |   .0312896   .0399405     0.78   0.434    -.0470621    .1096414
         13  |   .2945289   .0597977     4.93   0.000     .1772229    .4118349
         98  |  -.0784251   .0369164    -2.12   0.034    -.1508445   -.0060057
         99  |   .0021905   .0534152     0.04   0.967    -.1025947    .1069758
             |
        year |
       2011  |  -.0032179   .0346407    -0.09   0.926    -.0711729    .0647372
       2012  |   .0231746   .0346071     0.67   0.503    -.0447147    .0910639
       2013  |    .014677   .0343781     0.43   0.669     -.052763    .0821171
       2014  |  -.0045585   .0344522    -0.13   0.895    -.0721439    .0630269
       2015  |  -.0158171   .0347582    -0.46   0.649    -.0840027    .0523686
       2016  |  -.1128054   .0350028    -3.22   0.001    -.1814708   -.0441399
       2017  |  -.0945727   .0350703    -2.70   0.007    -.1633707   -.0257748
       2018  |  -.0917659   .0354157    -2.59   0.010    -.1612413   -.0222905
       2019  |   -.119499   .0358259    -3.34   0.001    -.1897791   -.0492189
             |
       _cons |  -.2661802   .1134704    -2.35   0.019    -.4887766   -.0435838
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.28    0.439006
      exvot1 |      2.52    0.396800
     votsom1 |      1.64    0.609460
      roe_ll |      1.15    0.872030
      tangib |      1.24    0.808019
   lnattotal |      1.54    0.647491
     catset1 |
          2  |      2.17    0.461250
          3  |      1.37    0.730343
          4  |      1.77    0.565241
          5  |      1.88    0.532453
          6  |      1.78    0.561453
          7  |      2.27    0.440828
          8  |      2.69    0.372103
          9  |      1.32    0.758651
         10  |      2.02    0.495005
         11  |      2.04    0.490838
         12  |      2.20    0.454328
         13  |      1.30    0.768379
         98  |      2.86    0.350102
         99  |      1.54    0.649160
        year |
       2011  |      1.87    0.533923
       2012  |      1.89    0.528271
       2013  |      1.91    0.522345
       2014  |      1.93    0.516978
       2015  |      1.97    0.507916
       2016  |      1.92    0.519641
       2017  |      1.90    0.527624
       2018  |      1.85    0.541994
       2019  |      1.81    0.552437
-------------+----------------------
    Mean VIF |      1.88

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   543.69
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub d_nm exvot1 votsom1 roe_ll tangib lnattotal i.catset1 i.year if
>  dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom1!=., 
> re nolog vce (robust)
. 
. regress dum_capsub d_nm exvot1 votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i.y
> ear if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & votsom
> 1!=.

      Source |       SS       df       MS              Number of obs =    1387
-------------+------------------------------           F( 30,  1356) =   10.10
       Model |  24.4235153    30  .814117177           Prob > F      =  0.0000
    Residual |  109.354423  1356  .080644854           R-squared     =  0.1826
-------------+------------------------------           Adj R-squared =  0.1645
       Total |  133.777938  1386  .096520879           Root MSE      =  .28398

------------------------------------------------------------------------------
  dum_capsub |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        d_nm |  -.0454998   .0250626    -1.82   0.070    -.0946655    .0036658
      exvot1 |  -.0280995   .0906976    -0.31   0.757    -.2060223    .1498233
     votsom1 |  -.1040193   .1320192    -0.79   0.431    -.3630034    .1549648
     vots1_2 |   .1371327   .1387382     0.99   0.323    -.1350321    .4092975
      roe_ll |  -.0573205   .0515324    -1.11   0.266    -.1584123    .0437714
      tangib |   .2723859   .0508818     5.35   0.000     .1725704    .3722014
   lnattotal |   .0307722   .0065131     4.72   0.000     .0179954     .043549
             |
     catset1 |
          2  |   .0926965   .0390675     2.37   0.018     .0160571    .1693358
          3  |  -.0652792   .0553885    -1.18   0.239    -.1739356    .0433772
          4  |  -.0160893    .047946    -0.34   0.737    -.1101458    .0779671
          5  |  -.0076889   .0433502    -0.18   0.859    -.0927297    .0773519
          6  |  -.0329824   .0468374    -0.70   0.481     -.124864    .0588992
          7  |  -.0901333   .0395624    -2.28   0.023    -.1677435   -.0125232
          8  |  -.1136911   .0395097    -2.88   0.004    -.1911979   -.0361843
          9  |  -.1216607    .063396    -1.92   0.055    -.2460255    .0027042
         10  |  -.0588176   .0401191    -1.47   0.143    -.1375199    .0198847
         11  |  -.0920382   .0454714    -2.02   0.043      -.18124   -.0028363
         12  |   .0292073   .0399963     0.73   0.465     -.049254    .1076687
         13  |   .2971193   .0598556     4.96   0.000     .1796997     .414539
         98  |  -.0820251   .0370959    -2.21   0.027    -.1547967   -.0092534
         99  |   .0035597   .0534336     0.07   0.947    -.1012618    .1083812
             |
        year |
       2011  |  -.0027434   .0346443    -0.08   0.937    -.0707056    .0652188
       2012  |   .0238072   .0346134     0.69   0.492    -.0440943    .0917088
       2013  |   .0149843   .0343798     0.44   0.663    -.0524591    .0824277
       2014  |   -.004791   .0344533    -0.14   0.889    -.0723786    .0627966
       2015  |  -.0160458   .0347593    -0.46   0.644    -.0842336     .052142
       2016  |  -.1134825   .0350098    -3.24   0.001    -.1821617   -.0448033
       2017  |  -.0951179    .035075    -2.71   0.007    -.1639249   -.0263108
       2018  |  -.0919613   .0354165    -2.60   0.010    -.1614383   -.0224842
       2019  |     -.1204   .0358378    -3.36   0.001    -.1907034   -.0500965
             |
       _cons |  -.2428997     .11589    -2.10   0.036    -.4702429   -.0155566
------------------------------------------------------------------------------

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
        d_nm |      2.35    0.425820
      exvot1 |      2.87    0.348688
     votsom1 |     15.99    0.062547
     vots1_2 |     17.08    0.058543
      roe_ll |      1.15    0.872028
      tangib |      1.25    0.802402
   lnattotal |      1.55    0.645861
     catset1 |
          2  |      2.17    0.461249
          3  |      1.37    0.729926
          4  |      1.79    0.558086
          5  |      1.88    0.531979
          6  |      1.78    0.560665
          7  |      2.27    0.440370
          8  |      2.70    0.371057
          9  |      1.32    0.757926
         10  |      2.02    0.494728
         11  |      2.07    0.483506
         12  |      2.21    0.453067
         13  |      1.30    0.766906
         98  |      2.88    0.346727
         99  |      1.54    0.648723
        year |
       2011  |      1.87    0.533821
       2012  |      1.89    0.528090
       2013  |      1.91    0.522302
       2014  |      1.93    0.516954
       2015  |      1.97    0.507894
       2016  |      1.93    0.519442
       2017  |      1.90    0.527494
       2018  |      1.85    0.541977
       2019  |      1.81    0.552080
-------------+----------------------
    Mean VIF |      2.89

. actest dum_capsub

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    173.324      1    0.0000 |   1 |    173.324      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest cgindex

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    627.665      1    0.0000 |   1 |    627.665      1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest exvot1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1162.308     1    0.0000 |   1 |    1162.308     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest votsom1

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1057.886     1    0.0000 |   1 |    1057.886     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. actest vots1_2

Cumby-Huizinga test for autocorrelation
  H0: disturbance is MA process up to order q
  HA: serial correlation present at specified lags >q
-----------------------------------------------------------------------------
  H0: q=0 (serially uncorrelated)        |  H0: q=specified lag-1
  HA: s.c. present at range specified    |  HA: s.c. present at lag specified
-----------------------------------------+-----------------------------------
    lags   |      chi2      df     p-val | lag |      chi2      df     p-val
-----------+-----------------------------+-----+-----------------------------
   1 -  1  |    1033.033     1    0.0000 |   1 |    1033.033     1    0.0000
-----------------------------------------------------------------------------
  Test requires conditional homoskedasticity

. estat hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of dum_capsub

         chi2(1)      =   544.20
         Prob > chi2  =   0.0000

. **xtlogit dum_capsub d_nm exvot1 votsom1 vots1_2 roe_ll tangib lnattotal i.catset1 i
> .year if dum_capsub !=. & cgindex !=. & roe_ll !=. & tangib!=. & lnattotal!=. & vots
> om1!=., re nolog vce (robust)

. log close
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